Incorporation Of Data Research Articles

Social Determinants of Health and Surgical Desirability of Outcome Ranking in Older Veterans

Evaluating how social determinants of health (SDOH) influence veteran outcomes is crucial, particularly for quality improvement. To measure associations between SDOH, care fragmentation, and surgical outcomes using a Desirability of Outcome Ranking (DOOR). This was a cohort study of US veterans using data from the Veterans Affairs (VA) Surgical Quality Improvement Program (VASQIP; 2013-2019) limited to patients aged 65 years or older with inpatient stays between 2 and 30 days, merged with multiple data sources, including Medicare. Race and ethnicity data were retrieved from VASQIP, Medicare and Medicaid beneficiary summary files, the Veterans Health Administration Corporate Data Warehouse, and the United States Veterans Eligibility Trends and Statistics file. Data were analyzed between September 2023 and February 2024. Living in a highly deprived neighborhood (Area Deprivation Index >85), race and ethnicity used as a social construct, rurality, and care fragmentation (percentage of non-VA care days). DOOR is a composite, patient-centered ranking of 26 outcomes ranging from no complication (1, best) to 90-day mortality or near-death complications (6, worst). A series of proportional odds regressions was used to assess the impact of SDOH and care fragmentation adjusted for clinical risk factors, including presentation acuity (presenting with preoperative acute serious conditions and urgent or emergent surgical procedures). The cohort had 93 644 patients (mean [SD] age, 72.3 [6.2] years; 91 443 [97.6%] male; 74 624 [79.7%] White). Veterans who identified as Black (adjusted odds ratio [aOR], 1.06; 95% CI, 1.02-1.10; P = .048) vs White and veterans with higher care fragmentation (per 20% increase in VA care days relative to all care days: aOR, 1.01; 95% CI, 1.01-1.02; P < .001) were associated with worse (higher) DOOR scores until adjusting for presentation acuity. Living in rural geographic areas was associated with better DOOR scores than living in urban areas (aOR, 0.93; 95% CI, 0.91-0.96; P < .001), and rurality was associated with lower presentation acuity (preoperative acute serious conditions: aOR, 0.88; 95% CI, 0.81-0.95; P = .001). Presentation acuity was higher in veterans identifying as Black, living in deprived neighborhoods, and with increased care fragmentation. Veterans identifying as Black and veterans with greater proportions of non-VA care had worse surgical outcomes. VA programs should direct resources to reduce presentation acuity among Black veterans, incentivize veterans to receive care within the VA where possible, and better coordinate veterans' treatment and records between care sources.

Do we mean the same? Semantic native-likeness in highly proficient second language users

Research question: Native-likeness (similarity between non-native and native users of the same language) depends on second language proficiency (L2p). However, evidence comes mainly from phonological or syntactic tasks, prompting an underexplored question: can higher L2p also entail a more native-like organization of semantic memory? Methodology: We asked high and low proficiency bilinguals to describe concepts in their second (English) and first (Spanish) language. We then compared their responses with semantic structure norms from native speakers of each language, and derived measures of semantic native-likeness through a validated semantic proximity measure. Data and analysis: We examined whether semantic native-likeness scores discriminated between high and low L2p levels at the group level (via inferential statistics) and at the individual level (via machine learning analyses). Findings: We found that semantic associations in English were more native-like in high than in low L2p bilinguals. Machine learning analyses showed that this pattern was highly consistent across individual subjects. Responses in Spanish did not differ between groups. Originality: This is the first study assessing semantic native-likeness through concept-feature associations in high and low L2p bilinguals, both at the group- and the individual-level. Significance: Our findings suggest that increased L2p entails a more native-like organization of semantic memory, informing models of bilingual cognition and its links with experience-related variables.

Big Data in online education

From classifying learners to predicting learner behaviour, the application of Big Data in online education has been vast. Besides the potential benefits of Big Data in education, it is necessary to critically engage with some ethical and social challenges that Big Data presents to the field of online learning. The increasing use of big data by large institutional actors and corporations raises questions not only about data privacy and ownership, but whether this data is used to genuinely improve learner and teacher online learning experiences, or primarily for commercial profits and institutional benefits. When addressing ethical concerns regarding the use of Big Data in education, critiques often follow a reasoning that is in line with corporate interests and neoliberal logic of marketization of education. Given the importance of the pursuit for democratic online education, the need for critical perspectives in the field is ever-more essential. This research tries to critically address the role and impact of Big Data on labour relations and economic fairness in online education by examining both corporate and institutional data practices in online learning. The study puts forward a provisional theory of the use of Big Data in two large online learning platforms (Coursera and Blackboard) using critical grounded theory. The core category of Exploitation of the learning community, the three constituent concepts; the Vendor-Institutional Complex, Use of learner generated value for profit, and the Behavioral monitoring and engineering; and the sustaining category, the Magic Trick, were the foundational blocks for developing an emancipatory theory that addressed ethical issues of economic fairness regarding the use of big data in online education.

Design and Development of Cybersecurity Suite

Protecting personal, corporate and government data in the digital age requires cybersecurity. Traditional cybersecurity methods sometimes lack comprehensive and dynamic protection mechanisms as cyber-attacks change and become more sophisticated. A new cybersecurity suite is proposed in this study. It integrates various advanced security technologies into a single user-friendly platform interaction. This paper specifically combines techniques for real-time phishing detection, strong password management, safe encryption and decryption services and an interactive module to promote user awareness and behaviours to improve digital security for individuals and organization. Rapid prototyping is used in iterative and incremental development phases to adapt to changing security needs and user input. This method allows the platform to be constantly improved to satisfy the current cybersecurity standards and react to new threats. The prototyped developed provides proactive digital defence, a complete cybersecurity platform that equips users to defend their digital presence. With the intuitive design and advanced feature set, the prototype aspires to democratize cybersecurity. Empirical testing of the prototype revealed that makes an effective protection of a wider audience without the complexities of security software. This paper contributes in the advances cybersecurity technology and shaping the digital protection measures.

Automated Versus Semi-Automated Lab Value Extraction for the VA Cardiac Surgical Quality Improvement Program

IntroductionThe Veterans Affairs Surgical Quality Improvement Program (VASQIP) trains surgical quality nurses (SQNs) at each Veterans Affairs (VA) hospital to extract or verify 187 variables from the medical record for all cardiac surgical cases. For ten preoperative laboratory values, VASQIP has a semiautomated (SA) system in which local lab values are automatically extracted, verified by SQNs, and lab values recorded at other VA facilities are manually extracted. The objective of this study was to develop and validate a method to automate the extraction of these ten preoperative laboratory values and compare results with the current SA method. Materials and methodsWe developed methods to extract ten preoperative laboratory values and measurement dates from the VA Corporate Data Warehouse using Logical Observation Identifiers Names and Codes. Automated (A) versus SA information extraction was compared in terms of agreement, conformance to data definitions, proximity to surgery, and missingness. ResultsFor surgeries with both A and SA lab values, the intraclass correlation coefficients for the ten variables ranged from 0.90 to 0.98. For several variables, the A method resulted in much lower rates of missing data (e.g., 2.4% versus 22.5% missing data for high-density lipoprotein) and eliminated out-of-date-range entries. ConclusionsAlthough SQN-extracted data are widely considered the gold standard within National Surgical Quality Improvement Programs, there may be advantages to fully automating extraction of lab values, including high congruence with SA SQN-extracted or verified values and lower rates of missingness and out-of-date-range data.

Evaluating equity in a national virtual care management intervention: Delivery and outcomes by race/ethnicity among Veterans with hypertension and diabetes.

To evaluate whether the Preventive Health Inventory (PHI)-a virtual care management intervention addressing hypertension and diabetes management implemented nationally in the Veterans Health Administration (VHA)-was delivered equitably among racial/ethnic groups and if existing inequities in hypertension and diabetes outcomes changed following PHI receipt. We used data from the VHA Corporate Data Warehouse among Veterans enrolled in primary care nationally from February 28, 2021 to March 31, 2022. We used logistic regression to evaluate PHI receipt and hypertension and diabetes outcomes after PHI implementation among Veterans with hypertension and/or diabetes. We conducted unadjusted analyses and analyses adjusting for clinic fixed effects using dummy variables. We identified Veterans engaged in primary care with documented race/ethnicity and hypertension and/or diabetes diagnoses in all months during the study period. Prior to PHI, Non-Hispanic Black (NHB) (42.2%) and Hispanic (39.5%) Veterans were less likely to have controlled hypertension vs. Non-Hispanic White (NHW) Veterans (47.5%); NHB Veterans (32.9%) were more likely to have uncontrolled diabetes vs. NHW Veterans (25.1%). Among 1,805,658 Veterans, 5.7% NHW (N = 68,744), 5.6% NHB (N = 22,580), 10.2% Hispanic (N = 13,313), 6.2% Asian/Pacific Islander/Native Hawaiian (N = 1868), 5.1% American Indian/Native Alaskan (N = 744), and 5.6% multiple races or other race (N = 1647) Veterans received PHI. We found no significant racial inequities in PHI receipt in unadjusted and adjusted models. Hypertension and diabetes measures improved more in the intervention group compared with the group who did not receive the intervention. There were no new or worsened inequities after PHI, and in pre-/post-intervention analysis, among NHB Veterans, the inequity in uncontrolled diabetes improved by 1.9 percentage points (95% CI 0.2, 3.6). Our findings suggest the PHI intervention was equitably deployed across race/ethnicity groups without significantly impacting most existing inequities in diabetes and hypertension.

Utilization of Glucagon-Like Peptide-1 Receptor Agonist and Dipeptidyl Peptidase-4 Inhibitors at the Veterans Health Administration.

Background: Glucagon-like peptide-1 receptor agonists (GLP-1RA) and dipeptidyl peptidase-4 inhibitors (DPP-4i) are incretin-based therapies commonly used in the management of type 2 diabetes. Public interest in GLP-1RA soared after discovering their ability to lower body weight in patients without diabetes. Objective: To examine recent trends in usage of GLP-1RA and DPP-4i in the Veterans Health Administration (VHA). Methods: We extracted GLP-1RA and DPP-4i use from the national VHA Corporate Data Workhouse (CDW) between fiscal years (FYs) 2011 to 2021, which encompass medication class, name, dosage, date of filled prescription, and patients' characteristics. Results: A total of 3 037 006 prescriptions for DPP-4i and 2 183 294 prescriptions for GLP-1RA were filled during FY 2011 to 2021. More patients were prescribed DPP-4i (273 002 subjects) compared with GLP-1RA (157 209 subjects) from FY 2011 to 2021. Overall, 10.7% used DPP-4i for 90 days or less in comparison to 9.1% in GLP-1RA (P < 0.001). The proportion of patients prescribed DPP-4i who were 75 years of age or older was relatively stable over the years 2011 to 2021 (mean proportion = 19%). However, the proportion of patients who were 75 years of age or older prescribed GLP-1RA increased from 4.2% in 2011 to 16.9% in 2021. Conclusions: Incretin-based therapies have become a well-established class of drugs within the VHA. Even though DPP-4i usage in older adults has remained stable over the past 10 years, prescriptions for GLP-1RA in older adults have increased multifold over the last few years, which might be attributed to recent trial evidence showing benefit in cardiovascular outcomes and weight reduction.

Medical training program size and clinical staff productivity and turnover.

The objective of this analysis was to evaluate the effect of resident program training size on clinician productivity and turnover in the Veterans Health Administration (VHA), the largest education and training platform for medical professionals in the United States. We retrieved administrative data on training programs and training facilities from the VA Office of Academic Affiliations and the VHA Corporate Data Warehouse. Data on primary care physician shortage areas were retrieved from the Health Resources and Services Administration. We used a quasi-experimental instrumental variables 2SLS design and constructed an exogenous predicted training allocation treatment variable as a function of the total national training program allocation. The outcome was clinical staff productivity and turnover. Secondary analyses stratified results using Health Professional Shortage Areas data (HPSA). Data were obtained for a national dataset of 141 VHA medical facilities and 26 specialties that hosted training programs across 11 years from 2011 to 2021 (N = 132,177). Instrumental variables results showed that on average, an increase of one training slot in a specialty leads to a decrease of 0.039 visits per standardized clinic day (p < 0.001) and a 0.02 percentage point increase in turnover (p < 0.001). The direction of this association varied by specialty: while psychiatry and psychology specialties saw a decline in productivity, fields such as primary care and cardiology experienced an increase in productivity. HPSA stratified results indicate that negative effects on productivity and turnover are driven by areas with little to no primary care physician shortage, whereas shortage areas experienced a small increase in productivity and no effect on turnover. This quasi-experimental evaluation indicates that resident training program size is associated with reduced productivity and increased turnover in specialties such as psychiatry and in facilities with high baseline productivity. However, in specialties like primary care and cardiology, as well as areas with shortages of primary care, larger training programs are associated with increased productivity.

Data-Driven Financial Strategy Transformation: The Impact of Corporate Data Capital Utilization on The Level of Financial Asset Allocation

Data-Driven Financial Strategy Transformation: The Impact of Corporate Data Capital Utilization on The Level of Financial Asset Allocation

The Influence of Active, Passive, and Manual Therapy Interventions on Escalation of Health Care Events After Physical Therapist Care in Veterans With Low Back Pain.

The objective of this study was to examine the associations between active, passive, and manual therapy interventions with the escalation-of-care events following physical therapist care for veterans with low back pain (LBP). A retrospective cohort study was conducted in 3618 veterans who received physical therapist care for LBP between January 1, 2015 and January 1, 2018. The Department of Veterans Affairs (VA) Corporate Data Warehouse was utilized to identify LBP-related physical therapist visits and procedures, as well as opioid prescription and non-physical therapy clinic encounters. The association between physical therapist interventions with 1-year escalation-of-care events were assessed using adjusted odds ratios from logistic regression. Nearly all veterans (98%) received active interventions, but only a minority (31%) received manual therapy. In the 1-year follow-up period, the odds of receiving an opioid prescription were 30% lower for those who received manual therapy in addition to active interventions, as compared with patients who received only active interventions. Moreover, the odds of receiving primary care, specialty care, and diagnostic testing were 30% to 130% higher for patients who received electrical stimulation or more than 1 passive intervention in addition to active treatments, as compared with patients who received only active interventions. The use of manual therapy along with active interventions was associated with reduced prescription of opioids, while utilization of specific passive interventions such as electrical stimulation or multiple modalities in conjunction with active interventions resulted in increased escalation-of-care events. The use of active interventions, which is supported by most clinical practice guidelines, was the cornerstone of physical therapist care for veterans with LBP. However, the use of clinical practice guideline-recommended manual therapy interventions was low but associated with reduced opioid prescriptions. The use of 2 or more different passive interventions along with active interventions was common (34%) and associated with less-than-optimal escalation-of-care outcomes.

The Use of Big Data in Corporate Accounting and Data Analysis: Opportunities and Challenges

IntroductionThe era of Big Data technologies is restructuring corporate accounting, enabling a wide array of dynamic potential. This project explores how Big Data affects financial management, focusing on forecasting, risk management, and technological advances.MethodsThis work is informed by a large-scale review of scholarly literature, industry reports, and case studies. Databases like Google Scholar, PubMed, IEEE Xplore, Scopus, and Web of Science were used for data collection. Keywords included Big Data, corporate accounting, financial forecasting, risk management, data analytics, AI in accounting, machine learning in finance, and blockchain technology applied to accounting. The review was structured thematically, focusing on financial forecasting, risk management, and ethical considerations affected by Big Data practices in this domain.ResultsBig Data improves financial forecasting accuracy using historical data, market trends, and consumer behavior analytics. In risk management, Big Data facilitates effective proactive actions through thorough risk evaluation. Emerging technologies are anticipated to automate complex tasks, enhance predictive analytics, and improve the security and reliability of financial transactions.ConclusionsBig Data holds significant potential for corporate accounting, though challenges such as managerial complexity, data privacy, and expertise requirements for handling large volumes of data remain. The study highlights the importance of flexibility and technological adaptability, as well as specialized skill sets. It calls for continual dialogue and policy development to meet the ethical challenges presented by Big Data/AI, promoting responsible deployment while ensuring fairness. This review contributes to academic discourse and provides strategic guidance for practitioners in the evolving landscape of corporate accounting

Minimum wage standards and corporate short-term and long-term investment preferences

This paper examines the impact of minimum wage levels in the regions where companies are located on their short-term and long-term investment preferences, using listed companies in China as the research subjects and combining micro corporate data with regional level data. The results show that higher minimum wage levels in the regions where companies are located have a significant inhibitory effect on their short-term investments, but not on their long-term investments. Further analysis shows that inhibitory effect of minimum wage levels on short-term investment by enterprises is mainly evident in non-state-owned enterprises and enterprises in regions with lower resource endowments.

Public enemies? The differential effects of reputation and celebrity on corporate misconduct scandalization

AbstractResearch SummaryWe explore misconduct scandalization's antecedents by focusing on the rational and emotional bases underlying reputation and celebrity, and considering how they can enhance or reduce the likelihood misconduct is scandalized as a function of the misconduct's objective and perceived severity. Specifically, we argue the quantifiable nature of objective misconduct severity enhances reputation's rational influence, but attenuates celebrity's emotion‐based appeal. Conversely, perceived misconduct severity reduces reputation's influence, while enhancing the media‐driven interest in celebrity firms' behaviors. Our findings based on corporate data breaches confirm that objective severity amplifies reputation's effect and attenuates celebrity's effect, while perceived severity amplifies celebrity's effect and attenuates reputation's effect. Our findings highlight the importance of social evaluations' sociocognitive content in understanding why only some misconduct becomes scandalized.Managerial SummaryCommitting misconduct is costly; having it scandalized is devastating. Yet little is known about how social evaluations influence why only some firms' misconduct is scandalized, beyond the vague notion that prominent firms' misconduct attracts media attention. We find that the rational and emotional bases of firms' evaluations matter. High reputation, based on the rational assessment of firms' capabilities, increases the likelihood of scandalization for objectively severe misconduct, and the influence of celebrity—originating from audiences' emotional resonance with firms' unconventional traits and behaviors—weakens as objective severity increases. Conversely, reputation's influence weakens, and celebrity's influence strengthens, as media “availability cascades” grow and increase perceived severity. In addition to providing a more realistic portrayal of media behavior, we offer insights into post‐misconduct communications and remedial actions.

Using social risks to predict unplanned hospital readmission and emergency care among hospitalized Veterans.

(1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index. We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program. We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans' health-related social risk. We included 156,690 Veterans' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022. The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA. Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.

The Use of DEA for ESG Activities and DEI Initiatives Considered as “Pillar of Sustainability” for Economic Growth Assessment in Western Balkans

Data envelopment analysis (DEA), which is frequently used in efficiency analysis, has also been applied to the measurement of entrepreneurial efficiency for the attainment of desired values of macroeconomic indicators (such as the objectives of sustainable economic growth). For this application, DEA takes into account the economic, environmental, and social impact of entrepreneurship as the three dimensions of sustainability. This paper aimed to investigate the potential for a scalable (in diversity, equity, and inclusion dimensions) DEA application in sustainable entrepreneurship performance (SEP) assessment through three channels (assessing SEP without ESG activities; ESG→SEP; ESG (DEI)→SEP) and present an empirical study related to economic growth assessment and its environmental, social, and governance (ESG), and diversity, equity and inclusion (DEI) determinants across selected Western Balkans (WB) and European Union (EU) companies, based on the use of the proposed scalable DEA. It highlights how crucial a scalable nonparametric approach to macroeconomic efficiency analysis is and provides a more comprehensive perspective to the researchers on this issue. This study used a non-oriented DEA model with variable return-to-scale in a group of 60 WB and 60 EU companies, all of which adopted ICT/Blockchain (BC) technologies (the 11 ESG metrics). The annual corporate data was collected for seven years from 2017 until 2023. We projected the selected data to three country particularities (mass acceptance, adoption, and implementation of ICT/BC; mass labor force return from overseas; and ethnic, cultural, and religious particularities) and performed statistical analysis. Our findings estimate the influence of these three particularities on economic growth potential. In all countries’ cases, we found a statistically sound (significant, positive) correlation between ESG and SEP’s economic growth quality performance. Particularly, when corporate social and DEI initiatives mediate (channel III), SEP’s economic growth gains the best performance (+18%) in countries with ethnic, cultural, and religious particularities (BiH, NM), a +17% in countries enjoying massive labor force return from overseas (AL) and performs well in quality (particularly in the innovation and integrity) SEP performance success dimensions (all WB and EU countries). The proposed scalable DEA shows clearly, by performing an empirical analysis, which modern business (adopting ICT/BC) is the most effective in achieving sustainability projected to country particularities, helping corporate management to improve economic growth efficiency.

Differences across race and ethnicity in the quality of antidepressant medication management.

To illustrate the importance of a multidimensional view of disparities in quality of antidepressant medication management (AMM), as well as discriminating "within-facility" disparities from disparities that exist between facilities. We used data from the Veterans Health Administration's (VA) Corporate Data Warehouse (CDW) which contains clinical and administrative data from VA facilities nationally. CDW data were used to measure five indicators of AMM quality, including the HEDIS Effective Acute-Phase and Effective Continuation-Phase measures. Mixed effects regression models were used to examine differences in quality indicators between racial/ethnic groups, controlling for other demographic and clinical factors. An adaptation of the Kitagawa-Blinder-Oaxaca (KBO) method was used to decompose mean differences in treatment quality between racial and ethnic groups into within- and between-facility effects. Demographic, clinical, and health service utilization data were extracted for patients in fiscal year 2017 with a diagnosis of depression and a new start of an antidepressant medication. The decomposition of the overall differences between White and Black patients on receiving an initial 90-day prescription (46.7% vs. 32.7%), Effective Acute-Phase (79.7% vs. 66.8%), and Effective Continuation-Phase (64.0% vs. 49.6%) HEDIS measures revealed that most of the overall effects were "within-facility," meaning that Black patients are less likely to meet these measures regardless of where they are treated. Although the overall magnitude of disparities between White and Hispanic patients on these three measures was very similar (46.7% vs. 32.7%; 79.7% vs. 69.2%; 64.0% vs. 53.6%), the differences were more attributable to Hispanic patients being treated in facilities with overall lower performance on these measures. Discriminating within- and between-facility disparities and taking a multidimensional view of quality are essential to informing efforts to address disparities in AMM quality.

Creative connections: the neural correlates of semantic relatedness are associated with creativity

The associative theory of creativity proposes that creative ideas result from connecting remotely related concepts in memory. Previous research found that higher creative individuals exhibit a more flexible organization of semantic memory, generate more uncommon word associations, and judge remote concepts as more related. In this study (N = 93), we used fMRI to investigate brain regions involved in judging the relatedness of concepts that vary in their semantic distance, and how such neural involvement relates to individual differences in creativity. Brain regions where activity increased with semantic relatedness mainly overlapped with default, control, salience, semantic control, and multiple demand networks. The default and semantic control networks exhibited increased involvement when evaluating more remote associations. Finally, higher creative people, who provided higher relatedness judgements on average, exhibited lower activity in those regions, possibly reflecting higher neural efficiency. We discuss these findings in the context of the neurocognitive processing underlying creativity. Overall, our findings indicate that judging remote concepts as related reflects a cognitive mechanism underlying creativity and shed light on the neural correlates of this mechanism.

Improving access to buprenorphine for rural veterans in a learning health care system.

To describe a learning health care system research process designed to increase buprenorphine prescribing for the treatment of opioid use disorder (OUD) in rural primary care settings within U.S. Department of Veterans Affairs (VA) treatment facilities. Using national administrative data from the VA Corporate Data Warehouse, we identified six rural VA health care systems that had improved their rate of buprenorphine prescribing within primary care from 2015 to 2020 (positive deviants). We conducted qualitative interviews with leaders, clinicians, and staff involved in buprenorphine prescribing within primary care from these sites to inform the design of an implementation strategy. Qualitative interviews to inform implementation strategy development. Interviews were audio-recorded, transcribed verbatim, and coded by a primary coder and secondary reviewer. Analysis utilized a mixed inductive/deductive approach. To develop an implementation strategy, we matched clinical needs identified within interviews with resources and strategies participants had utilized to address these needs in their own sites. Interview participants (n = 30) identified key clinical needs and strategies for implementing buprenorphine in rural, primary care settings. Common suggestions included the need for clinical mentorship or a consult service, buprenorphine training, and educational resources. Building upon interview findings and in partnership with a clinical team, we developed an implementation strategy composed of an engaging case-based training, an audit and feedback process, and educational resources (e.g., Buprenorphine Frequently Asked Questions, Rural Care Model Infographic). We describe a learning health care system research process that leveraged national administrative data, health care provider interviews, and clinical partnership to develop an implementation strategy to encourage buprenorphine prescribing in rural primary care settings.

Integrating Buprenorphine for Opioid Use Disorder into Rural, Primary Care Settings.

Medications for opioid use disorder (MOUD) including buprenorphine are effective, but underutilized. Rural patients experience pronounced disparities in access. To reach rural patients, the US Department of Veterans Affairs (VA) has sought to expand buprenorphine prescribing beyond specialty settings and into primary care. Although challenges remain, some rural VA health care systems have begun offering opioid use disorder (OUD) treatment with buprenorphine in primary care. We conducted interviews with clinicians, leaders, and staff within these systems to understand how this outcome had been achieved. Using administrative data from the VA Corporate Data Warehouse (CDW), we identified rural VA health care systems that had improved their rate of primary care-based buprenorphine prescribing over the period 2015-2020. We conducted qualitative interviews (n = 30) with staff involved in implementing or prescribing buprenorphine in these systems to understand the processes that had facilitated implementation. Clinicians, staff, and leaders embedded within rural VA health care systems located in the Northwest, West, Midwest (2), South, and Northeast. Qualitative interviews were analyzed using a mixed inductive/deductive approach. Interviews revealed the processes through which buprenorphine was integrated into primary care, as well as processes insufficient to enact change. Implementation was often initially catalyzed through a targeted hire. Champions then engaged clinicians and leaders one-on-one to "pitch" the case, describe concordance between buprenorphine prescribing and existing goals, and delineate the supportive role that they could provide. Sites were prepared for implementation by developing new clinical teams and redesigning clinical processes. Each of these processes was made possible with the active, instrumental support of leadership. Results suggest that rural systems seeking to improve buprenorphine accessibility in primary care may need to alter primary care structures to accommodate buprenorphine prescribing, whether through new hires, team development, or clinical redesign.

Antimicrobial Use in Veterans Affairs Community Living Centers, 2015 - 2019

Background: Optimizing antimicrobial use (AU) among post-acute and long-term care (PALTC) residents is fundamental to reducing the morbidity and mortality associated with multidrug-resistant organism (MDROs), as well as unintended social consequences related to infection prevention. Data on AU in PALTC settings remains limited. The U.S. Department of Veteran Affairs (VA) provides PALTC to over 23,000 residents at 134 community living centers (CLCs) across the United States annually. Here, we describe AU in VA CLCs, assessing both class and length of therapy. Methods: Monthly AU between January 1, 2015 and December 31, 2019 was extracted from the VA Corporate Data Warehouse across 134 VA CLCs. Antimicrobials and administration routes were based on the National Healthcare Safety Network AU Option protocol for hospitals. Rates of AU were measured as the days of therapy (DOT) per 1,000 resident-days. An antimicrobial course was defined as the same drug and route administered to the same resident with a gap of ≤ three days between administrations. Course duration was measured in days. AU Rates were measured as the days of therapy (DOT) per 1,000 resident-days. Results: The most common class of antimicrobial course administered during the study period was beta-lactam/beta-lactamase inhibitor combinations (15%) followed by fluroquinolones (14%), extended-spectrum cephalosporins (12%) and glycopeptides (11%; Figure 1). Neuraminidase inhibitors had the longest median (IQR) course duration (10 (IQR 8) days), followed by tetracyclines (8 (IQR 8) days), and then folate pathway inhibitors, nitrofurans and 1st/2nd generation cephalosporins (7 (IQR 7) days). Overall, 60% of antimicrobial courses were administered orally, with fluroquinolones the most frequently administered orally (20%). From 2015 – 2019, the annual rate of total antimicrobial use across VA CLCs decreased slightly from 213.6 to 202.5 DOT/1,000 resident-days. During the 5-year study period, fluroquinolone use decreased from 27.47 to 13.36 DOTs/1,000 resident-days. First and 2nd generation cephalosporin use remained relatively stable, but 3rd or greater generation cephalosporin use increased from 14.70 to 19.21 DOTs/1,000 resident-days (Figure 2). Conclusion: The marked decrease in the use of fluoroquinolones at VA CLCs from 2015-2019 is similar to patterns observed for VA hospitals and for non-VA PALTC facilities. The overall use of antibacterial agents at VA CLCs decreased slightly during the study period, but other broad-spectrum agents such as 3rd or greater generation cephalosporins increased over the same period. The strategies used to decrease fluroquinolone use may have application for other antibiotic classes, both in VA and non-VA PALTC settings.Disclosure: Robin Jump: Research support to my institution from Merck and Pfizer; Advisory boards for Pfizer