Predictive modeling of housing instability and homelessness in the Veterans Health Administration

OBJECTIVE: To develop and test predictive models of housing instability and homelessness based on responses to a brief screening instrument administered throughout the Veterans Health Administration (VHA). DATA SOURCES/STUDY SETTING: Electronic medical record data from 5.8 million Veterans who responded to the VHA's Homelessness Screening Clinical Reminder (HSCR) between October 2012 and September 2015. STUDY DESIGN: We randomly selected 80% of Veterans in our sample to develop predictive models. We evaluated the performance of both logistic regression and random forests—a machine learning algorithm—using the remaining 20% of cases. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from two sources: VHA's Corporate Data Warehouse and National Homeless Registry. PRINCIPAL FINDINGS: Performance for all models was acceptable or better. Random forests models were more sensitive in predicting housing instability and homelessness than logistic regression, but less specific in predicting housing instability. Rates of positive screens for both outcomes were highest among Veterans in the top strata of model‐predicted risk. CONCLUSIONS: Predictive models based on medical record data can identify Veterans likely to report housing instability and homelessness, making the HSCR screening process more efficient and informing new engagement strategies. Our findings have implications for similar instruments in other health care systems.U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Grant/Award Number: IIR 13-334 (IIR 13-334 - U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSRD))Accepted manuscrip

Predicting the Risk of Falling with Artificial Intelligence

Predicting the Risk of Falling with Artificial Intelligence Abstract Background: Fall prevention is a huge patient safety concern among all healthcare organizations. The high prevalence of patient falls has grave consequences, including the cost of care, longer hospital stays, unintentional injuries, and decreased patient and staff satisfaction. Preventing a patient from falling is critical in maintaining a patient’s quality of life and averting the high cost of healthcare expenses. Local Problem: Two hospitals\u27 healthcare system saw a significant increase in inpatient falls. The fall rate is one of the nursing quality indicators, and fall reduction is a key performance indicator of high-quality patient care. Methods: This quality improvement evidence-based observational project compared the rate of fall (ROF) between the experimental and control unit. Pearson’s chi-square and Fisher’s exact test were used to analyze and compare results. Qualtrics surveys evaluated the nurses’ perception of AI, and results were analyzed using the Mann-Whitney Rank Sum test. Intervention. Implementing an artificial intelligence-assisted fall predictive analytics model that can timely and accurately predict fall risk can mitigate the increase in inpatient falls. Results: The pilot unit (Pearson’s chi-square = p pp\u3c0.001). Conclusions: AI-assisted automatic fall predictive risk assessment produced a significant reduction if the number of falls, the ROF, and the use of fall countermeasures. Further, nurses’ perception of AI improved after the introduction of FPAT and presentation

Academic Health Science Centers and Health Disparities: A Qualitative Review of the Intervening Role of the Electronic Health Record and Social Determinants of Health

Literature on the magnitude of negative health outcomes from health disparities is voluminous. Defined as the health effects of racism, environmental injustice, forms of discrimination, biases in science, and sociological or socioeconomic predictors across populations, health disparities are part of an ongoing and complicated national problem that health equity programs are specifically designed to address. Academic Health Science Centers (AHC) institutions are a complex and unique educational-healthcare ecosystem that often serves as a safety net for patients in vulnerable and lower-income communities. These institutions are often viewed as one of the most uniquely positioned entities in the U.S. with an abundance of resources and networks to advance health equity as a high-impact goal and strategic imperative. Relatively little progress, however, has been made to better understand the potentially transformative nature of how digital health technologies (DHT)—such as mobile health apps, electronic health record (EHR) and electronic medical record (EMR) systems, smart ‘wearable’ devices, artificial intelligence, and machine learning—may be optimized to better capture and analyze social determinants of health (SDH) data elements in order to inform strategies to address health disparities. Even less has been explored about the challenging implementation of electronic SDH screening and data capture processes within AHCs and how they are used to better inform decisions for patient and community care. This research examines how AHC institutions, as complex education-healthcare bureaucracies, have prioritized this specific challenge amongst many other competing incentives and agendas in order to ultimately develop better evidence-based strategies to advance health equity. While there are clear moral, ethical, and clinical motives for improving health outcomes for vulnerable populations, when an AHC demonstrates that electronically screening and capturing SDH can improve the ability to understand the “upstream” factors impacting their patients\u27 health outcomes, this can inform and influence policy-level choices in government legislation directed at community-level factors. A qualitative thematic analysis of interview data from AHC administrators and leadership illustrates how AHCs have mobilized their EHR as a featured component of their healthcare delivery system to address health disparities, exposing other related, multifactorial dimensions of the Institution and region. Key findings indicated that: electronic SDH screening and updating workflow processes within an AHC’s clinical enterprise is a significant venture with multiple risks and the potential of failure. Universal adoption and awareness of SDH screening is hampered by notions of hesitancy, skepticism, and doubt as to an AHC’s ability to meaningfully extract and use the data for decision-support systems. Additional investment in resources and incentive structures for capturing SDH are needed for continued monitoring of patient health inequalities and community social factors. Data from this and future replicated studies can be used to inform AHC and government decisions around health and social protection, planning, and policy

The Relationship Between Situational Crime Prevention Theory and Campus Employee Computer Misuse

Computer misuse is a leading problem for all industry sectors, including higher education. However, much of the current research related to computer misuse has been conducted in the business sector, leaving higher education a relatively unstudied group. Many theories have been addressed in computer security literature, but only one theory offers a more holistic solution to combating computer misuse, Situational Crime Prevention Theory. Situational Crime Prevention Theory encompasses four categories of countermeasures: countermeasures that Increase the Perceived Effort of the offender, countermeasures that Increase the Perceived Risk of the offender, countermeasures that Reduce the Anticipated Rewards of the offender, and countermeasures that Remove the Excuses to offend. This study endeavored to investigate whether a relationship exists between the categories of ountermeasures found in Situational Crime Prevention and the actual number of computer misuse incidents reported by CIO\u27s of public, four-year colleges and universities. Using a web-accessible, anonymous questionnaire, CIO\u27s of 442 public, four-year colleges and universities were asked to provide information related to the countermeasures that they have in place at their institutions and the number of insider computer misuse incidents their institutions experienced in the year 2009. The data were analyzed with PLS-Graph software to include composite reliability, t statistic and critical value analysis, and R-square analysis. Results showed a significant relationship between two out of four categories of countermeasures and the actual number of computer misuse incidents. These results would be particularly useful to administrators in higher education who are responsible for designing a technology security plan that is focused and cost-effective

K-5 Educators\u27 Perceptions of the Role of Speech Language Pathologists

Rarely is a school-based speech language pathologist (SLP) thought of as an active contributor to the achievement of students or to the learning community in general. Researchers have found benefits for students when members of the learning community collaborate, and the SLP should be a part of this community collaboration. This qualitative case study examined elementary school teachers\u27, administrators\u27, and reading specialists\u27 perspectives related to knowledge of and the inclusion of the SLP in the learning community at a local elementary school in central Georgia. Schon\u27s theory of reflective practice and Coleman\u27s theory of social capital provided the conceptual framework. Via an open-ended questionnaire and intensive interviews, 8 educators with 3 or more years of experience in 1 of the K-5 elementary schools in this local community provided data for this study. Data were recorded, transcribed, and analyzed through inductive methods using open and axial coding with thematic analysis. The results of the study showed 4 common themes that the participants felt were important. These themes included the fact that teachers understood the SLP to be a resource, but were unsure how to access their specialty; teachers and SLPs needed allotted time to work together; teachers and SLPs needed to communicate frequently; and teachers desired more knowledge of the SLP\u27s role in the educational setting. Important implications for social change in elementary school learning communities include increasing involvement of the SLP, promoting SLP involvement in the identification of at-risk students, increasing educator awareness of the SLP\u27s benefit, and increasing collaboration between SLPs and educators promoted through a 3-day professional learning project

Paradox in Paradise: Hidden Health Inequities on California's Central Coast

Examines the health of people living in the Central Coast region of California, and provides data about the area's increasingly diversified population and challenged health delivery system

Alternate Payment Models for Ryan White HIV/AIDS Program Funded Services: Strategies Used by Nine Grantees

The Health Resources and Services Administration (HRSA) HIV/AIDS Bureau (HAB) offers Ryan White HIV/AIDS Program (RWHAP) Part A and Part B grantees some flexibility in determining the method used for paying subgrantees for core medical and support services. Many Part A and Part B grantees use a traditional “cost-based reimbursement” approach, in which subgrantees submit budgets that include personnel costs, other direct costs related to the provision of funded services, and capped indirect costs (IDCs). Some grantees, however, have developed alternative reimbursement models for core medical and/or support services. This report summarizes the reimbursement approaches taken by nine RWHAP grantees. While not an exhaustive list, the seven Part A and two Part B grantees demonstrate a range of payment methods that might provide ideas for other grantees

Temporary Assistance for Needy Families policy manual

This policy manual gives details of SNAP in South Carolina