Assessing Risk Literacy Skills: Enhancing Healthcare Management among University Students.

The purpose of this study was to explore the determinants of risk literacy among university students in the United States by utilizing the Berlin Numeracy Test. Risk literacy skills are essential for decision-making and communication of risks, but few studies consider university students. This study aims to evaluate the association of sociodemographic factors with individual risk literacy levels. An observational cross-sectional survey study was used with a convenience sample of 184 undergraduate and graduate university students. Statistical analysis revealed significant differences for demographics at risk for negative outcomes associated with lower risk literacy. For this group of students, the majority had below-average numeracy. These findings can guide healthcare professionals to focus on college-age individuals with low-risk literacy scores to enhance patient understanding, facilitate communication, and promote healthier behaviors.

Effects of Internal and External Factors on Hospital Data Breaches: Quantitative Study.

BACKGROUND: Health care data breaches are the most rapidly increasing type of cybercrime; however, the predictors of health care data breaches are uncertain. OBJECTIVE: This quantitative study aims to develop a predictive model to explain the number of hospital data breaches at the county level. METHODS: This study evaluated data consolidated at the county level from 1032 short-term acute care hospitals. We considered the association between data breach occurrence (a dichotomous variable), predictors based on county demographics, and socioeconomics, average hospital workload, facility type, and average performance on several hospital financial metrics using 3 model types: logistic regression, perceptron, and support vector machine. RESULTS: The model coefficient performance metrics indicated convergent validity across the 3 model types for all variables except bad debt and the factor level accounting for counties with >20% and up to 40% Hispanic populations, both of which had mixed coefficient directionality. The support vector machine model performed the classification task best based on all metrics (accuracy, precision, recall, F1-score). All the 3 models performed the classification task well with directional congruence of weights. From the logistic regression model, the top 5 odds ratios (indicating a higher risk of breach) included inpatient workload, medical center status, pediatric trauma center status, accounts receivable, and the number of outpatient visits, in high to low order. The bottom 5 odds ratios (indicating the lowest odds of experiencing a data breach) occurred for counties with Black populations of >20% and <40%, >80% and <100%, and >40% but <60%, as well as counties with ≤20% Asian or between 80% and 100% Hispanic individuals. Our results are in line with those of other studies that determined that patient workload, facility type, and financial outcomes were associated with the likelihood of health care data breach occurrence. CONCLUSIONS: The results of this study provide a predictive model for health care data breaches that may guide health care managers to reduce the risk of data breaches by raising awareness of the risk factors.

Social determinants of health literacy: a cross-sectional exploratory study.

Despite the importance of health literacy to health-promoting behaviors, few studies have assessed the social determinants of health literacy in a random sample of individuals from the USA. The study evaluated the association of sociodemographic factors with individual health literacy levels. This cross-sectional web-based observational study utilized the Health Literacy Questionnaire (HLQ), a multidimensional instrument measuring nine areas of literacy. Multivariate regression results revealed several factors associated with HLQ scores such as self-rated health rating, frequency of visits to healthcare providers, smoking, gender and rural versus urban residence. Low health literacy was associated with lower self-rated overall health and with less frequent visits to healthcare providers. Males scored higher on engaging with health providers, navigating, understanding the health system and understanding health information well enough to know what to do. These findings can guide healthcare professionals to focus on individuals from groups having lower health literacy scores to promote healthy behaviors.

An Exploratory Analysis of the Association between Hospital Quality Measures and Financial Performance.

Hospitals are perpetually challenged by concurrently improving the quality of healthcare and maintaining financial solvency. Both issues are among the top concerns for hospital executives across the United States, yet some have questioned if the efforts to enhance quality are financially sustainable. Thus, the aim of this study is to examine if efforts to improve quality in the hospital setting have a corresponding association with hospital profitability. Recent and directly relevant research on this topic is very limited, leaving practitioners uncertain about the wisdom of their investments in interventions which enhance quality and patient safety. We assessed if eight different quality measures were associated with our targeted measure of hospital profitability: the net patient revenue per adjusted discharge. Using multivariate regression, we found that improving quality was significantly associated with our targeted measure of hospital profitability: the net patient revenue per adjusted discharge. Significant findings were reported for seven of eight quality measures tested, including the HCAHPS Summary Star Rating (p < 0.001), Hospital Compare Overall Rating (p < 0.001), All-Cause Hospital-Wide Readmission Rate (p < 0.01), Total Performance Score (p < 0.001), Safety Domain Score (p < 0.01), Person and Community Engagement Domain Score (p < 0.001), and the Efficiency and Cost Reduction Score (p < 0.001). Failing to address quality and patient safety issues is costly for US hospitals. We believe our findings support the premise that increased attention to the quality of care delivered as well as patients' perceptions of care may allow hospitals to accentuate profitability and advance a hospital's financial position.

The Health Status of the US Veterans: A Longitudinal Analysis of Surveillance Data Prior to and during the COVID-19 Pandemic.

Chronic diseases affect a disproportionate number of United States (US) veterans, causing significant long-term health issues and affecting entitlement spending. This longitudinal study examined the health status of US veterans as compared to non-veterans pre- and post-COVID-19, utilizing the annual Center for Disease Control and Prevention (CDC) behavioral risk factor surveillance system (BRFSS) survey data. Age-adjusted descriptive point estimates were generated independently for 2003 through 2021, while complex weighted panel data were generated from 2011 and onward. General linear modeling revealed that the average US veteran reports a higher prevalence of disease conditions except for mental health disorders when compared to a non-veteran. These findings were consistent with both pre- and post-COVID-19; however, both groups reported a higher prevalence of mental health issues during the pandemic years. The findings suggest that there have been no improvements in reducing veteran comorbidities to non-veteran levels and that COVID-19 adversely affected the mental health of both populations.

Examining Predictors of Myocardial Infarction.

Cardiovascular diseases are the leading cause of death in the United States. This study analyzed predictors of myocardial infarction (MI) for those aged 35 and older based on demographic, socioeconomic, geographic, behavioral, and risk factors, as well as access to healthcare variables using the Center for Disease (CDC) Control Behavioral Risk Factor Surveillance System (BRFSS) survey for the year 2019. Multiple quasibinomial models were generated on an 80% training set hierarchically and then used to forecast the 20% test set. The final training model proved somewhat capable of prediction with a weighted F1-Score = 0.898. A complete model based on statistically significant variables using the entirety of the dataset was compared to the same model built on the training set. Models demonstrated coefficient stability. Similar to previous studies, age, gender, marital status, veteran status, income, home ownership, employment status, and education level were important demographic and socioeconomic predictors. The only geographic variable that remained in the model was associated with the West North Central Census Division (in-creased risk). Statistically important behavioral and risk factors as well as comorbidities included health status, smoking, alcohol consumption frequency, cholesterol, blood pressure, diabetes, stroke, chronic obstructive pulmonary disorder (COPD), kidney disease, and arthritis. Three access to healthcare variables proved statistically significant: lack of a primary care provider (Odds Ratio, OR = 0.853, p < 0.001), cost considerations prevented some care (OR = 1.232, p < 0.001), and lack of an annual checkup (OR = 0.807, p < 0.001). The directionality of these odds ratios is congruent with a marginal effects model and implies that those without MI are more likely not to have a primary provider or annual checkup, but those with MI are more likely to have missed care due to the cost of that care. Cost of healthcare for MI patients is associated with not receiving care after accounting for all other variables.

Deep Vision for Breast Cancer Classification and Segmentation.

(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.

Determinants of Diabetes Disease Management, 2011-2019.

This study estimated the effects of Medicaid Expansion, demographics, socioeconomic status (SES), and health status on disease management of diabetes over time. The hypothesis was that the introduction of the ACA and particularly Medicaid Expansion would increase the following dependent variables (all proportions): (1) provider checks of HbA1c, (2) provider checks of feet, (3) provider checks of eyes, (4) patient education, (5) annual physician checks for diabetes, (6) patient self-checks of blood sugar. Data were available from the Behavioral Risk Factor Surveillance System for 2011 to 2019. We filtered the data to include only patients with diagnosed non-gestational diabetes of age 45 or older (n = 510,991 cases prior to weighting). Linear splines modeled Medicaid Expansion based on state of residence as well as implementation status. Descriptive time series plots showed no major changes in proportions of the dependent variables over time. Quasibinomial analysis showed that implementation of Medicaid Expansion had a statistically negative effect on patient self-checks of blood sugar (odds ratio = 0.971, p < 0.001), a statistically positive effect on physician checks of HbA1c (odds ratio = 1.048, p < 0.001), a statistically positive effect on feet checks (odds ratio = 1.021, p < 0.001), and no other significant effects. Evidence of demographic, SES, and health status disparities existed for most of the dependent variables. This finding was especially significant for HbA1c checks by providers. Barriers to achieving better diabetic care remain and require innovative policy interventions.

Big-Data Skills: Bridging the Data Science Theory-Practice Gap in Healthcare.

Demand for big-data scientists continues to escalate driving a pressing need for new graduates to be more fluent in the big-data skills needed by employers. If a gap exists between the educational knowledge held by graduates and big data workplace skills needed to produce results, workers will be unable to address the big data needs of employers. This survey explores big-data skills in the classroom and those required in the workplace to determine if a skills gap exists for big-data scientists. In this work, data was collected using a national survey of healthcare professionals. Participant responses were analyzed to inform curriculum development, providing valuable information for academics and the industry leaders who hire new data talent.

Are college students health literate?

Objective: To examine the health literacy of college students. Participants: A convenience sample of 245 graduate and undergraduate college students. Methods: During February-April of 2018 participants completed the Short Test of Functional Health Literacy which assessed literacy on two passages describing a thyroid scan, and basic healthcare insurance information. Results: Most college students displayed adequate health literacy (99.2%). The ANOVA analyses revealed college classification was the most significant predictor, followed by ethnicity, sex, and primary language. Age was significantly related to health literacy, when holding college classification constant. Interestingly, college major, healthcare work experience, or having health care credentials were not predictors of health literacy. Conclusion: This group of college students had adequate health literacy skills. However, the results of this study revealed demographic disparities that suggest further study.

Managing Security Risk: Modeling the Root Causes of Data Breaches.

Health care data breaches are occurring at unprecedented rates, but breach causes are challenging to identify. The purpose of this exploratory study was to identify potential root causes associated with health care data breaches and to create a model of potential data breach factors to inform risk assessment and future predictive analysis. We considered organizational factors, business processes, and technological tools that may be associated with health care data breach occurrences. Using legal requirements, security industry frameworks, and health care standards, we developed a testable health care data breach model. This security model can inform managers who are working to conduct risk assessments, allocate resources, and minimize security risks.

Cyber-Analytics: Identifying Discriminants of Data Breaches.

In this study, the relationship between data breach characteristics and the number of individuals affected by these violations was considered. Data were acquired from the Department of Health and Human Services breach reporting database and analyzed using SPSS. Regression analyses revealed that the hacking/IT incident breach type and network server breach location were the most significant predictors of the number of individuals affected; however, they were not predictive when combined. Moreover, network server location and unauthorized access/disclosure breach type were predictive when combined. Additional analyses of variance revealed that covered entity type and business associate presence were significant predictors, while the geographic region of a breach occurrence was insignificant. The results of this study revealed several associations between healthcare breach characteristics and the number of individuals affected, suggesting that more individuals are affected in hacking/IT incidents and network server breaches independently and that network server breach location and unauthorized access/disclosure breach type were predictive in combination.

Big Data Analytics in Healthcare: Investigating the Diffusion of Innovation.

The shortage of data scientists has restricted the implementation of big data analytics in healthcare facilities. This survey study explores big data tool and technology usage, examines the gap between the supply and the demand for data scientists through Diffusion of Innovations theory, proposes engaging academics to accelerate knowledge diffusion, and recommends adoption of curriculum-building models. For this study, data were collected through a national survey of healthcare managers. Results provide practical data on big data tool and technology skills utilized in the workplace. This information is valuable for healthcare organizations, academics, and industry leaders who collaborate to implement the necessary infrastructure for content delivery and for experiential learning. It informs academics working to reengineer their curriculum to focus on big data analytics. The paper presents numerous resources that provide guidance for building knowledge. Future research directions are discussed.

Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50.

BACKGROUND: Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

Mobile Device Security: Perspectives of Future Healthcare Workers.

Healthcare data breaches on mobile devices continue to increase, yet the healthcare industry has not adopted mobile device security standards. This increase is disturbing because individuals are often accessing patients' protected health information on personal mobile devices, which could lead to a data breach. This deficiency led the researchers to explore the perceptions of future healthcare workers regarding mobile device security. To determine healthcare students' perspectives on mobile device security, the investigators designed and distributed a survey based on the Technology Threat Avoidance Theory. Three hundred thirty-five students participated in the survey. The data were analyzed to determine participants' perceptions about security threats, effectiveness and costs of safeguards, self-efficacy, susceptibility, severity, and their motivation and actions to secure their mobile devices. Awareness of interventions to protect mobile devices was also examined. Results indicate that while future healthcare professionals perceive the severity of threats to their mobile data, they do not feel personally susceptible. Additionally, participants were knowledgeable about security safeguards, but their knowledge of costs and problems related to the adoption of these measures was mixed. These findings indicate that increasing security awareness of healthcare professionals should be a priority.

The Odds of Success: Predicting Registered Health Information Administrator Exam Success.

The purpose of this study was to craft a predictive model to examine the relationship between grades in specific academic courses, overall grade point average (GPA), on-campus versus online course delivery, and success in passing the Registered Health Information Administrator (RHIA) exam on the first attempt. Because student success in passing the exam on the first attempt is assessed as part of the accreditation process, this study is important to health information management (HIM) programs. Furthermore, passing the exam greatly expands the graduate's job possibilities because the demand for credentialed graduates far exceeds the supply of credentialed graduates. Binary logistic regression was utilized to explore the relationships between the predictor variables and success in passing the RHIA exam on the first attempt. Results indicate that the student's cumulative GPA, specific HIM course grades, and course delivery method were predictive of success.

Usability of Web-based Personal Health Records: An Analysis of Consumers' Perspectives.

Personal health records (PHRs) have many benefits, including the ability to increase involvement of patients in their care, which provides better healthcare outcomes. Although issues related to usability of PHRs are a significant barrier to adoption, there is a paucity of research in this area. Thus, the researchers explored consumers' perspective on the usability of two commercially available web-based PHRs. Data from the Usefulness, Satisfaction, and Ease of Use questionnaire were collected from a sample of health information management students (N = 90). A one-way analysis of variance (ANOVA) showed that Microsoft HealthVault had higher scores in most usability categories when compared to Health Companion. Study results indicated that PHR developers should evaluate Microsoft HealthVault as a model for improving PHR usability.