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.

Geospatial analysis as a tool to identify target areas for Chagas disease education for healthcare providers.

Chagas Disease (CD) is a neglected zoonotic disease of the Americas. It can be fatal if not diagnosed and treated in its early stages. Using geospatial and sensitivity analysis, this study focuses on understanding how to better allocate resources and educational information to areas in the United States, specifically Texas, that have the potential for increased risk of CD cases and the associated costs of addressing the disease. ICD-9 and 10 inpatient hospital diagnostic codes were used to illustrate the salience of potentially missed CD diagnoses (e.g., cardiomyopathic diagnoses) and where these are occurring with more frequency. Coding software along with GIS and Microsoft Excel 3D mapping were used to generate maps to illustrate where there may be a need for increased statewide surveillance and screening of populations at greater risk for CD. The CD cases reported to the Texas Department of State Healthcare Services (TxDSHS) are not homogenously dispersed throughout the state but rather, reveal that the incidences are in clusters and primarily in urban areas, where there is increased access to physician care, CD research and diagnostic capabilities.

Association of Electronic Health Record Vendors With Hospital Financial and Quality Performance: Retrospective Data Analysis.

BACKGROUND: Electronic health records (EHRs) are a central feature of care delivery in acute care hospitals; however, the financial and quality outcomes associated with system performance remain unclear. OBJECTIVE: In this study, we aimed to evaluate the association between the top 3 EHR vendors and measures of hospital financial and quality performance. METHODS: This study evaluated 2667 hospitals with Cerner, Epic, or Meditech as their primary EHR and considered their performance with regard to net income, Hospital Value-Based Purchasing Total Performance Score (TPS), and the unweighted subdomains of efficiency and cost reduction; clinical care; patient- and caregiver-centered experience; and patient safety. We hypothesized that there would be a difference among the 3 vendors for each measure. RESULTS: None of the EHR systems were associated with a statistically significant financial relationship in our study. Epic was positively associated with TPS outcomes (R2=23.6%; ß=.0159, SE 0.0079; P=.04) and higher patient perceptions of quality (R2=29.3%; ß=.0292, SE 0.0099; P=.003) but was negatively associated with patient safety quality scores (R2=24.3%; ß=-.0221, SE 0.0102; P=.03). Cerner and Epic were positively associated with improved efficiency (R2=31.9%; Cerner: ß=.0330, SE 0.0135, P=.01; Epic: ß=.0465, SE 0.0133, P<.001). Finally, all 3 vendors were associated with positive performance in the clinical care domain (Epic: ß=.0388, SE 0.0122, P=.002; Cerner: ß=.0283, SE 0.0124, P=.02; Meditech: ß=.0273, SE 0.0123, P=.03) but with low explanatory power (R2=4.2%). CONCLUSIONS: The results of this study provide evidence of a difference in clinical outcome performance among the top 3 EHR vendors and may serve as supportive evidence for health care leaders to target future capital investments to improve health care delivery.

Competency assessment of an undergraduate program using a third-party, objective pre-post examination.

BACKGROUND: Assessing competencies or program learning outcomes in educational programs is often a leadership challenge. This case study reports medical education program's efforts to document undergraduate competency attainment using a pre-post, third-party, objective testing service that allows for inter-university comparison, a testing service that is being adopted by some certification and accrediting bodies. METHODS: Students completed a pre-test after program acceptance and a post-test at the end of the last didactic semester (1.5 years later) just prior to their required internships. Scores and subscores were evaluated using t-tests (Holm-adjusted p-values). MANOVA models of sub-competency difference scores were also evaluated. RESULTS: Results indicate competency improvement for each of the 12 areas based on the n = 55 student sample, (p < .001 for all scores). These improvements were independent of ethnicity, age, gender, and grades. The average student improved by 12.85 points (95% CI of 10.52 to 15.18) with the largest improvements in strategic planning and leadership competency areas (21.30 and 18.33 percentage points, respectively). CONCLUSIONS: The third-party pre-post has some face validity given that student performance improved after completing a related curriculum as would be expected. Congruent with earlier studies, we find that repeated testing helps document competency attainment and that a single method for assessment is insufficient. We further document limitations of this 3d-party exam.

Assessing the effectiveness of Chagas disease education for healthcare providers in the United States.

BACKGROUND: Chagas disease is a zoonotic infection caused by the parasite Trypanosoma cruzi, which affects an estimated 8-11 million people globally. Chagas disease is almost always associated with poverty in rural areas and disproportionately impacts immigrants from Latin America living in the United States. Approximately 20-30% of people who are infected with Chagas disease will develop a chronic form of the infection that can be fatal if left untreated. Chagas disease is vastly underestimated in the United States, often goes undiagnosed and is not well understood by most U.S. healthcare providers. One of the most important ways at reducing barriers to improving diagnostics of Chagas disease in the U.S. is giving healthcare providers the most up-to-date information and access to leading experts. METHODS: An online webinar was conducted for healthcare providers, veterinarians and public health professionals using Chagas disease expert panelists. Pre and post tests were administered to participants (n = 57) to determine the efficacy in raising awareness and to determine key focus areas for improving knowledge. A Wilcoxon rank-sum was used for non-parametric variables equivalent and for questions that assessed knowledge the McNemar's Chi-Square test was used. RESULTS: There were statistically significant learning increases in multiple categories including transmission (p = <.001), clinical presentation (p = 0.016), diagnostics (p = <.001), and treatment (p = <.001). CONCLUSION: Providing easily accessible learning opportunities using validated testing and evaluations should be further developed for rural healthcare providers in the U.S. as well as healthcare providers serving under represented populations such as immigrants. There is a clear lack of knowledge and awareness surrounding Chagas disease in the United States and just by raising awareness and providing education on the topic, lives will be saved.

Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models.

BACKGROUND: Hospital-based back surgery in the United States increased by 60% from January 2012 to December 2017, yet the supply of neurosurgeons remained relatively constant. During this time, adult obesity grew by 5%. OBJECTIVE: This study aimed to evaluate the demand and associated costs for hospital-based back surgery by geolocation over time to evaluate provider practice variation. The study then leveraged hierarchical time series to generate tight demand forecasts on an unobserved test set. Finally, explanatory financial, technical, workload, geographical, and temporal factors as well as state-level obesity rates were investigated as predictors for the demand for hospital-based back surgery. METHODS: Hospital data from January 2012 to December 2017 were used to generate geospatial-temporal maps and a video of the Current Procedural Terminology codes beginning with the digit 63 claims. Hierarchical time series modeling provided forecasts for each state, the census regions, and the nation for an unobserved test set and then again for the out-years of 2018 and 2019. Stepwise regression, lasso regression, ridge regression, elastic net, and gradient-boosted random forests were built on a training set and evaluated on a test set to evaluate variables important to explaining the demand for hospital-based back surgery. RESULTS: Widespread, unexplained practice variation over time was seen using geographical information systems (GIS) multimedia mapping. Hierarchical time series provided accurate forecasts on a blind dataset and suggested a 6.52% (from 497,325 procedures in 2017 to 529,777 in 2018) growth of hospital-based back surgery in 2018 (529,777 and up to 13.00% by 2019 [from 497,325 procedures in 2017 to 563,023 procedures in 2019]). The increase in payments by 2019 are estimated to be US $323.9 million. Extreme gradient-boosted random forests beat constrained and unconstrained regression models on a 20% unobserved test set and suggested that obesity is one of the most important factors in explaining the increase in demand for hospital-based back surgery. CONCLUSIONS: Practice variation and obesity are factors to consider when estimating demand for hospital-based back surgery. Federal, state, and local planners should evaluate demand-side and supply-side interventions for this emerging problem.

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

Teaching Principal Components Using Correlations.

Introducing principal components (PCs) to students is difficult. First, the matrix algebra and mathematical maximization lemmas are daunting, especially for students in the social and behavioral sciences. Second, the standard motivation involving variance maximization subject to unit length constraint does not directly connect to the "variance explained" interpretation. Third, the unit length and uncorrelatedness constraints of the standard motivation do not allow re-scaling or oblique rotations, which are common in practice. Instead, we propose to motivate the subject in terms of optimizing (weighted) average proportions of variance explained in the original variables; this approach may be more intuitive, and hence easier to understand because it links directly to the familiar "R-squared" statistic. It also removes the need for unit length and uncorrelatedness constraints, provides a direct interpretation of "variance explained," and provides a direct answer to the question of whether to use covariance-based or correlation-based PCs. Furthermore, the presentation can be made without matrix algebra or optimization proofs. Modern tools from data science, including heat maps and text mining, provide further help in the interpretation and application of PCs; examples are given. Together, these techniques may be used to revise currently used methods for teaching and learning PCs in the behavioral sciences.

College students' willingness to accept COVID-19 vaccines.

Objective To assess college students' willingness to accept COVID-19 vaccines and the factors that influence their decisions. Participants: Traditional (aged 18-23) undergraduate students at a university in central Texas. Methods: An online survey was administered in fall 2020 to 614 students stratified by sex and race/ethnicity. Results: 40.9% of students planned to take the vaccine as soon as possible, 37.1% eventually, 11.4% only if required, and 10.6% did not intend to be vaccinated. Analyses indicated that gender, major/minor, political affiliation, receiving a flu shot in the preceding 12 months, perception of risk for COVID-19, and vaccine hesitancy were all associated with willingness to accept COVID-19 vaccines. Conclusion: Results confirm that no one-size-fits-all approach to promoting COVID-19 vaccination among college students is possible. Instead, administrators interested in increasing vaccine uptake should address concerns of specific groups, while also utilizing the prosocial beliefs of college students (e.g., being vaccinated will protect others).

The Predictive Factors of Hospital Bankruptcy-An Exploratory Study.

The United States healthcare industry has witnessed a number of hospitals declare bankruptcy. This has a meaningful impact on local communities with vast implications on access, cost, and quality of care available. In our research, we seek to determine what contemporary structural and operational factors influence a bankruptcy outcome, and craft predictive models to guide healthcare leaders on how to best avoid bankruptcy in the future. In this exploratory study we performed, a single-year cross-sectional analysis of short-term acute care hospitals in the United States and subsequently developed three predictive models: logistic regression, a linear support vector machine (SVM) model with hinge function, and a perceptron neural network. Data sources include Definitive Healthcare and Becker's Hospital Review 2019 report with 3121 observations of 32 variables with 27 observed bankruptcies. The three models consistently indicate that 18 variables have a significant impact on predicting hospital bankruptcy. Currently, there is limited literature concerning financial forecasting models and knowledge detailing the factors associated with hospital bankruptcy. By having tailored knowledge of predictive factors to establish a sound financial structure, healthcare institutions at large can be empowered to take proactive steps to avoid financial distress at the organizational level and ensure long-term financial viability.

An Exploratory Analysis of the Association Between Hospital Labor Costs and the Quality of Care.

Introduction: The competent healing touch of a caregiver is a critical component to the care patients receive. The more skilled the provider, the higher the likelihood outcomes will be delivered in a safe and effective manner. Unfortunately, in recent years, hospitals in the United States have faced immense financial pressures that are threatening their economic sustainability and patients' access to care in the future. Through the recent COVID-19 pandemic, the cost of delivering healthcare has continued to escalate, while the demand for patient care has exceeded many hospitals' capacity. Most troubling is the impact that the pandemic has had on the healthcare workforce, which has resulted in many hospitals struggling to fill vacancies at ever-increasing cost - all while under immense pressure to deliver quality patient care. What remains uncertain is whether the increase in labor costs has been matched with a commensurate rise in the quality of care or if quality has deteriorated as the labor force mix has changed to include more contract and temporary staff. Thus, in the enclosed study, we sought to determine what association, if any, exists between hospitals' cost of labor and the quality of care delivered. Methods and Models: Drawing from a representative national sample of nearly 3214 short-term acute care hospitals' common quality measures from the year 2021, we examined the labor cost-quality relationship via multivariate linear and logistic regression and found there is a persistent negative association across all quality outcome variables studied. Discussion: These findings lead us to believe simply paying more for hospital labor does not, by itself, ensure a positive patient outcome. We contend cautious use of temporary staff, measured adoption of short-term financial incentives, and robust staff development all should be considered as features of future workforce planning.

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.

Patient satisfaction as a function of in-house versus contract staffing models in Veterans Affairs community-based outpatient clinics.

As part of decision support for Central Arkansas Veterans Healthcare System, this research evaluated patient satisfaction differences associated with contract versus Veterans Affairs (VA) staffing of community-based outpatient clinics (CBOCs). The research question was as follows: What differences exist between contract versus VA-staffed outpatient clinics in the areas of patient perceptions of access, continuity of care, courtesy, education and information, emotional support, overall coordination, visit coordination, and patient preferences? The quarterly Survey of Healthcare Experiences of Patients provided satisfaction data necessary for this study. Results of Bonferonni-corrected z-tests of proportions indicated that the VA-staffed CBOCs had statistically higher scores for continuity of care, education and information, emotional support, overall coordination, and patient preferences. No statistically significant difference emerged for patient perception of access, courtesy, and visit coordination. The lack of finding associated with visit coordination coupled with the statistically significant difference for overall coordination suggests that coordination for non-VA-staffed CBOCs erodes after the patient encounter. In toto, patients reported higher satisfaction with in-house staffing solutions versus contractor solutions. The authors recommend additional cost-utility research to identify which of the two models, VA-staffed or non-VA-staffed, best meets the needs of all stakeholders.

The Future of Vertical Lift: initial insights for aircraft capability and medical planning.

The U.S. Army continues to evaluate capabilities associated with the Future of Vertical Lift (FVL) program-a futures program (with a time horizon of 15 years and beyond) intended to replace the current helicopter fleet. As part of the FVL study, we investigated required capabilities for future aeromedical evacuation platforms. This study presents two significant capability findings associated with the future aeromedical evacuation platform and one doctrinal finding associated with medical planning for future brigade operations. The three results follow: (1) Given simplifying assumptions and constraints for a scenario where a future brigade is operating in a 300 x 300 km2, the zero-risk aircraft ground speed required for the FVL platform is 350 nautical miles per hour (knots); (2) Given these same assumptions and constraints with the future brigade projecting power in a circle of radius 150 km, the zero-risk ground speed required for the FVL platform is 260 knots; and (3) Given uncertain casualty locations associated with future brigade stability and support operations, colocating aeromedical evacuation assets and surgical elements mathematically optimizes the 60-minute set covering problem.

Financial performance monitoring of the technical efficiency of critical access hospitals: a data envelopment analysis and logistic regression modeling approach.

From 1980 to 1999, rural designated hospitals closed at a disproportionally high rate. In response to this emergent threat to healthcare access in rural settings, the Balanced Budget Act of 1997 made provisions for the creation of a new rural hospital--the critical access hospital (CAH). The conversion to CAH and the associated cost-based reimbursement scheme significantly slowed the closure rate of rural hospitals. This work investigates which methods can ensure the long-term viability of small hospitals. This article uses a two-step design to focus on a hypothesized relationship between technical efficiency of CAHs and a recently developed set of financial monitors for these entities. The goal is to identify the financial performance measures associated with efficiency. The first step uses data envelopment analysis (DEA) to differentiate efficient from inefficient facilities within a data set of 183 CAHs. Determining DEA efficiency is an a priori categorization of hospitals in the data set as efficient or inefficient. In the second step, DEA efficiency is the categorical dependent variable (efficient = 0, inefficient = 1) in the subsequent binary logistic regression (LR) model. A set of six financial monitors selected from the array of 20 measures were the LR independent variables. We use a binary LR to test the null hypothesis that recently developed CAH financial indicators had no predictive value for categorizing a CAH as efficient or inefficient, (i.e., there is no relationship between DEA efficiency and fiscal performance).

Military health system efficiency: a review of history and recommendations for the future.

This article reviews the history of measuring military medical health care efficiency. No single approved definition or uniform framework has ever been offered or suggested defining military medical treatment facility efficiency over the last 225 years within the Department of Defense. The purpose of this article is to consolidate much of the existing research on the latent variable of military medical efficiency over the last two centuries, and to provide health care leaders a framework for understanding past and current practices in measuring efficiency in the military health care setting.

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.

Testing Kissick's Iron Triangle-Structural Equation Modeling Analysis of a Practical Theory.

BACKGROUND/PURPOSE: The purpose of this research is to determine if the tradeoffs that Kissick proposed among cost containment, quality, and access remain as rigidly interconnected as originally conceived in the contemporary health care context. Although many have relied on the Kissick model to advocate for health policy decisions, to our knowledge the model has never been empirically tested. Some have called for policy makers to come to terms with the premise of the Kissick model tradeoffs, while others have questioned the model, given the proliferation of quality-enhancing initiatives, automation, and information technology in the health care industry. One wonders whether these evolutionary changes alter or disrupt the originality of the Kissick paradigms themselves. METHODS: Structural equation modeling (SEM) was used to evaluate the Kissick hypothetical relationships among the unobserved constructs of cost, quality, and access in hospitals for the year 2018. Hospital data were obtained from Definitive Healthcare, a subscription site that contains Medicare data as well as non-Medicare data for networks, hospitals, and clinics (final n = 2766). RESULTS: Reporting significant net effects as defined by our chosen study variables, we find that as quality increases, costs increase, as access increases, quality increases, and as access increases, costs increase. Policy and Practice Implications: Our findings lend continued relevance to a balanced approach to health care policy reform efforts. Simultaneously bending the health care cost curve, increasing access to care, and advancing quality of care is as challenging now as it was when the Kissick model was originally conceived.

Accreditation and Certification: Do They Improve Hospital Financial and Quality Performance?

The relationship between healthcare organizational accreditation and their leaders' professional certification in healthcare management is of specific interest to institutions of higher education and individuals in the healthcare management field. Since academic program accreditation is one piece of evidence of high-quality education, and since professional certification is an attestation to the knowledge, skills, and abilities of those who are certified, we expect alumni who graduated from accredited programs and obtained professional certification to have a positive impact on the organizations that they lead, compared with alumni who did not graduate from accredited programs and who did not obtain professional certification. The authors' analysis examined the impact of hiring graduates from higher education programs that held external accreditation from the Commission on Accreditation of Healthcare Management Education (CAHME). Graduates' affiliation with the American College of Healthcare Executives (ACHE) professional healthcare leadership organization was also assessed as an independent variable. Study outcomes focused on these graduates' respective healthcare organization's performance measures (cost, quality, and access) to assess the researchers' inquiry into the perceived value of a CAHME-accredited graduate degree in healthcare administration and a professional ACHE affiliation. The results from this study found no effect of CAHME accreditation or ACHE affiliation on healthcare organization performance outcomes. The study findings support the need for future research surrounding healthcare administration professional graduate degree program characteristics and leader development affiliations, as perceived by various industry stakeholders.

A Report Card on Prevention Efforts of COVID-19 Deaths in US.

COVID-19 (otherwise known as coronavirus disease 2019) is a life-threatening pandemic that has been combatted in various ways by the government, public health officials, and health care providers. These interventions have been met with varying levels of success. Ultimately, we question if the preventive efforts have reduced COVID-19 deaths in the United States. To address this question, we analyze data pertaining to COVID-19 deaths drawn from the Centers for Disease Control and Prevention (CDC). For this purpose, we employ incidence rate restricted Poisson (IRRP) as an underlying analysis methodology and evaluate all preventive efforts utilized to attempt to reduce COVID-19 deaths. Interpretations of analytic results and graphical visualizations are used to emphasize our various findings. Much needed modifications of the public health policies with respect to dealing with any future pandemics are compiled, critically assessed, and discussed.