Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.

Importance: Inpatient clinical deterioration is associated with substantial morbidity and mortality but may be easily missed by clinicians. Early warning scores have been developed to alert clinicians to patients at high risk of clinical deterioration, but there is limited evidence for their effectiveness. Objective: To evaluate the effectiveness of an artificial intelligence deterioration model-enabled intervention to reduce the risk of escalations in care among hospitalized patients using a study design that facilitates stronger causal inference. Design, Setting, and Participants: This cohort study used a regression discontinuity design that controlled for confounding and was based on Epic Deterioration Index (EDI; Epic Systems Corporation) prediction model scores. Compared with other observational research, the regression discontinuity design facilitates causal analysis. Hospitalized adults were included from 4 general internal medicine units in 1 academic hospital from January 17, 2021, through November 16, 2022. Exposure: An artificial intelligence deterioration model-enabled intervention, consisting of alerts based on an EDI score threshold with an associated collaborative workflow among nurses and physicians. Main Outcomes and Measures: The primary outcome was escalations in care, including rapid response team activation, transfer to the intensive care unit, or cardiopulmonary arrest during hospitalization. Results: During the study, 9938 patients were admitted to 1 of the 4 units, with 963 patients (median [IQR] age, 76.1 [64.2-86.2] years; 498 males [52.3%]) included within the primary regression discontinuity analysis. The median (IQR) Elixhauser Comorbidity Index score in the primary analysis cohort was 10 (0-24). The intervention was associated with a -10.4-percentage point (95% CI, -20.1 to -0.8 percentage points; P = .03) absolute risk reduction in the primary outcome for patients at the EDI score threshold. There was no evidence of a discontinuity in measured confounders at the EDI score threshold. Conclusions and Relevance: Using a regression discontinuity design, this cohort study found that the implementation of an artificial intelligence deterioration model-enabled intervention was associated with a significantly decreased risk of escalations in care among inpatients. These results provide evidence for the effectiveness of this intervention and support its further expansion and testing in other care settings.

Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.

BACKGROUND: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. METHODS: Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. RESULTS: Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05). CONCLUSIONS: Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.

Relationship Between Patient Portal Tool Use and Medication Adherence and Viral Load Among Patients Living with HIV.

BACKGROUND: Patient portals play an increasingly critical role in engaging patients in their health care. They have the potential to significantly impact the health of those living with chronic diseases, such as HIV, for whom consistent care engagement is both critical and complex. OBJECTIVE: The primary aim was to examine the longitudinal relationships between individual portal tool use and health-related outcomes in patients living with HIV. DESIGN: Retrospective cohort study using electronic health record data to examine the relationship between patient portal tool use and key HIV-specific, health-related outcomes in patients engaged in care in the Veterans Health Administration (VA) through the application of marginal structural models. PARTICIPANTS: A national sample of patients living with HIV (PLWH) active in VA care who were registered to use the VA's patient portal, My HealtheVet (MHV; n = 18,390) between 10/1/2012 and 4/1/2017. MAIN MEASURES: The MHV tools examined were prescription refill (including prescription refill of an antiretroviral (ART) medication and any medication), secure messaging, view appointments, and view labs. Primary outcomes were viral load test receipt, viral load suppression, and ART medication adherence (measured as proportion of days covered). KEY RESULTS: The use of prescription refill for any medication or for ART was positively associated with ART adherence. Secure messaging was positively associated with ART adherence but not with viral load test receipt or viral load suppression. The use of view appointments was positively associated with ART adherence and viral load test receipt but not viral load suppression. The use of view labs was positively associated with viral load suppression but not ART adherence or viral load test receipt. CONCLUSIONS: These findings highlight the valuable role patient portals may play in improving health-related outcomes among PLWH and have implications for patients living with other types of chronic disease.

Hypertension Control During the Coronavirus Disease 2019 Pandemic: A Cohort Study Among US Veterans.

DESIGN: Retrospective cohort study. OBJECTIVE: We sought to examine whether disruptions in follow-up intervals contributed to hypertension control. BACKGROUND: Disruptions in health care were widespread during the coronavirus disease 2019 pandemic. PATIENTS AND METHODS: We identified a cohort of individuals with hypertension in both prepandemic (March 2019-February 2020) and pandemic periods (March 2020-February 2022) in the Veterans Health Administration. First, we calculated follow-up intervals between the last prepandemic and first pandemic blood pressure measurement during a primary care clinic visit, and between measurements in the prepandemic period. Next, we estimated the association between the maintenance of (or achieving) hypertension control and the period using generalized estimating equations. We assessed associations between follow-up interval and control separately for periods. Finally, we evaluated the interaction between period and follow-up length. RESULTS: A total of 1,648,424 individuals met the study inclusion criteria. Among individuals with controlled hypertension, the likelihood of maintaining control was lower during the pandemic versus the prepandemic (relative risk: 0.93; 95% CI: 0.93, 0.93). Longer follow-up intervals were associated with a decreasing likelihood of maintaining controlled hypertension in both periods. Accounting for follow-up intervals, the likelihood of maintaining control was 2% lower during the pandemic versus the prepandemic. For uncontrolled hypertension, the likelihood of gaining control was modestly higher during the pandemic versus the prepandemic (relative risk: 1.01; 95% CI: 1.01, 1.01). The likelihood of gaining control decreased with follow-up length during the prepandemic but not pandemic. CONCLUSIONS: During the pandemic, longer follow-up between measurements contributed to the lower likelihood of maintaining control. Those with uncontrolled hypertension were modestly more likely to gain control in the pandemic.

"Diving in the deep-end and swimming": a mixed methods study using normalization process theory to evaluate a learning collaborative approach for the implementation of palliative care practices in hemodialysis centers.

BACKGROUND: Normalization Process Theory (NPT) is an implementation theory that can be used to explain how and why implementation strategies work or not in particular circumstances. We used it to understand the mechanisms that lead to the adoption and routinization of palliative care within hemodialysis centers. METHODS: We employed a longitudinal, mixed methods approach to comprehensively evaluate the implementation of palliative care practices among ten hemodialysis centers participating in an Institute for Healthcare Improvement Breakthrough- Series learning collaborative. Qualitative methods included longitudinal observations of collaborative activities, and interviews with implementers at the end of the study. We used an inductive and deductive approach to thematic analysis informed by NPT constructs (coherence, cognitive participation, collective action, reflexive monitoring) and implementation outcomes. The NoMAD survey, which measures NPT constructs, was completed by implementers at each hemodialysis center during early and late implementation. RESULTS: The four mechanisms posited in NPT had a dynamic and layered relationship during the implementation process. Collaborative participants participated because they believed in the value and legitimacy of palliative care for patients receiving hemodialysis and thus had high levels of cognitive participation at the start. Didactic Learning Sessions were important for building practice coherence, and sense-making was solidified through testing new skills in practice and first-hand observation during coaching visits by an expert. Collective action was hampered by limited time among team members and practical issues such as arranging meetings with patients. Reflexive monitoring of the positive benefit to patient and family experiences was key in shifting mindsets from disease-centric towards a patient-centered model of care. NoMAD survey scores showed modest improvement over time, with collective action having the lowest scores. CONCLUSIONS: NPT was a useful framework for understanding the implementation of palliative care practices within hemodialysis centers. We found a nonlinear relationship among the mechanisms which is reflected in our model of implementation of palliative care practices through a learning collaborative. These findings suggest that the implementation of complex practices such as palliative care may be more successful through iterative learning and practice opportunities as the mechanisms for change are layered and mutually reinforcing. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04125537 . Registered 14 October 2019 - Retrospectively registered.

Expansion of Abdominal Aortic Aneurysm Screening and Ultrasound Utilization and Diagnosis.

This cohort study uses a deidentified national administrative claims database to assess the association of eligibility expansion with abdominal aortic aneurysm screening and diagnosis.

Types of Engagement Strategies to Engage High-Risk Patients in VA.

BACKGROUND: Many healthcare systems seek to improve care for complex high-risk patients, but engaging such patients to actively participate in their healthcare can be challenging. OBJECTIVE: To identify and describe types of patient engagement strategies reported as successfully deployed by providers/teams and experienced by patients in a Veterans Health Administration (VA) intensive primary care (IPC) pilot program. METHODS: We conducted semi-structured qualitative telephone interviews with 29 VA IPC staff (e.g., physicians, nurses, psychologists) and 51 patients who had at least four IPC team encounters. Interviews were recorded, transcribed, and analyzed thematically using a combination a priori/inductive approach. RESULTS: The engagement strategies successfully deployed by the IPC providers/teams could be considered either more "facilitative," i.e., facilitated by and dependent on staff actions, or more "self-sustaining," i.e., taught to patients, thus cultivating their ongoing patient self-care. Facilitative strategies revolved around enhancing patient access and coordination of care, trust-building, and addressing social determinants of health. Self-sustaining strategies were oriented around patient empowerment and education, caregiver and/or community support, and boundaries and responsibilities. When patients described their experiences with the "facilitative" strategies, many discussed positive proximal outcomes (e.g., increased access to healthcare providers). Self-sustaining strategies led to positive (self-reported) longer-term clinical outcomes, such as behavior change. CONCLUSION: We identified two categories of strategies for successfully engaging complex, high-risk patients: facilitative and self-sustaining. Intensive primary care program leaders may consider thoughtfully building "self-sustaining" engagement strategies into program development. Future research can confirm their effectiveness in improving health outcomes.

Cost of Care for Patients With Cirrhosis.

INTRODUCTION: There are limited longitudinal data on the cost of treating patients with cirrhosis, which hampers value-based improvement initiatives. METHODS: We conducted a retrospective cohort study of patients with cirrhosis seen in the Veterans Affairs health care system from 2011 to 2015. Patients were followed up through 2019. We identified a sex-matched and age-matched control cohort without cirrhosis. We estimated incremental annual health care costs attributable to cirrhosis for 4 years overall and in subgroups based on severity (compensated, decompensated), cirrhosis complications (ascites, encephalopathy, varices, hepatocellular cancer, acute kidney injury), and comorbidity (Deyo index). RESULTS: We compared 39,361 patients with cirrhosis with 138,964 controls. The incremental adjusted costs for caring of patients with cirrhosis were $35,029 (95% confidence interval $32,473-$37,585) during the first year and ranged from $14,216 to $17,629 in the subsequent 3 years. Cirrhosis complications accounted for most of these costs. Costs of managing patients with hepatic encephalopathy (year 1 cost, $50,080) or ascites ($50,364) were higher than the costs of managing patients with varices ($20,488) or hepatocellular cancer ($37,639) in the first year. Patients with acute kidney injury or those who had multimorbidity were the most costly at $64,413 and $66,653 in the first year, respectively. DISCUSSION: Patients with cirrhosis had substantially higher health care costs than matched controls and multimorbid patients had even higher costs. Cirrhosis complications accounted for most of the excess cost, so preventing complications has the largest potential for cost saving and could serve as targets for improvement.

Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring.

BACKGROUND: Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states. METHODS: Five machine learning models were developed and validated across seven US states (90-10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression. RESULTS: We evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination. CONCLUSIONS: A machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing.

Postoperative opioid prescribing patients with diabetes: Opportunities for personalized pain management.

BACKGROUND: Opioids are commonly prescribed for postoperative pain, but may lead to prolonged use and addiction. Diabetes impairs nerve function, complicates pain management, and makes opioid prescribing particularly challenging. METHODS: This retrospective observational study included a cohort of postoperative patients from a multisite academic health system to assess the relationship between diabetes, pain, and prolonged opioid use (POU), 2008-2019. POU was defined as a new opioid prescription 3-6 months after discharge. The odds that a patient had POU was assessed using multivariate logistic regression controlling for patient factors (e.g., demographic and clinical factors, as well as prior pain and opiate use). FINDINGS: A total of 43,654 patients were included, 12.4% with diabetes. Patients with diabetes had higher preoperative pain scores (2.1 vs 1.9, p<0.001) and lower opioid naïve rates (58.7% vs 68.6%, p<0.001). Following surgery, patients with diabetes had higher rates of POU (17.7% vs 12.7%, p<0.001) despite receiving similar opioid prescriptions at discharge. Patients with Type I diabetes were more likely to have POU compared to other patients (Odds Ratio [OR]: 2.22; 95% Confidence Interval [CI]:1.69-2.90 and OR:1.44, CI: 1.33-1.56, respectively). INTERPRETATION: In conclusion, surgical patients with diabetes are at increased risk for POU even after controlling for likely covariates, yet they receive similar postoperative opiate therapy. The results suggest a more tailored approach to diabetic postoperative pain management is warranted.

Awakening and Breathing Coordination: A Mixed-Methods Analysis of Determinants of Implementation.

Rationale: Routine spontaneous awakening and breathing trial coordination (SAT/SBT) improves outcomes for mechanically ventilated patients, but adherence varies. Understanding barriers to and facilitators of consistent daily use of SAT/SBT (implementation determinants) can guide the development of implementation strategies to increase adherence to these evidence-based interventions. Objectives: We conducted an explanatory, sequential mixed-methods study to measure variation in the routine daily use of SAT/SBT and to identify implementation determinants that might explain variation in SAT/SBT use across 15 intensive care units (ICUs) in urban and rural locations within an integrated, community-based health system. Methods: We described the patient population and measured adherence to daily use of coordinated SAT/SBT from January to June 2021, selecting four sites with varied adherence levels for semistructured field interviews. We conducted key informant interviews with critical care nurses, respiratory therapists, and physicians/advanced practice clinicians (n = 55) from these four sites between October and December 2021 and performed content analysis to identify implementation determinants of SAT/SBT use. Results: The 15 sites had 1,901 ICU admissions receiving invasive mechanical ventilation (IMV) for ⩾24 hours during the measurement period. The mean IMV patient age was 58 years, and the median IMV duration was 5.3 days (interquartile range, 2.5-11.9). Coordinated SAT/SBT adherence (within 2 h) was estimated at 21% systemwide (site range, 9-68%). ICU clinicians were generally familiar with SAT/SBT but varied in their knowledge and beliefs about what constituted an evidence-based SAT/SBT. Clinicians reported that SAT/SBT coordination was difficult in the context of existing ICU workflows, and existing protocols did not explicitly define how coordination should be performed. The lack of an agreed-upon system-level measure for tracking daily use of SAT/SBT led to uncertainty regarding what constituted adherence. The effects of the COVID-19 pandemic increased clinician workloads, impacting performance. Conclusions: Coordinated SAT/SBT adherence varied substantially across 15 ICUs within an integrated, community-based health system. Implementation strategies that address barriers identified by this study, including knowledge deficits, challenges regarding workflow coordination, and the lack of performance measurement, should be tested in future hybrid implementation-effectiveness trials to increase adherence to daily use of coordinated SAT/SBT and minimize harm related to the prolonged use of mechanical ventilation and sedation.

Myocardial Infarction Across COVID-19 Pandemic Phases: Insights From the Veterans Health Affairs System.

Background Cardiovascular procedural treatments were deferred at scale during the COVID-19 pandemic, with unclear impact on patients presenting with non-ST-segment-elevation myocardial infarction (NSTEMI). Methods and Results In a retrospective cohort study of all patients diagnosed with NSTEMI in the US Veterans Affairs Healthcare System from January 1, 2019 to October 30, 2022 (n=67 125), procedural treatments and outcomes were compared between the prepandemic period and 6 unique pandemic phases: (1) acute phase, (2) community spread, (3) first peak, (4) post vaccine, (5) second peak, and (6) recovery. Multivariable regression analysis was performed to assess the association between pandemic phases and 30-day mortality. NSTEMI volumes dropped significantly with the pandemic onset (62.7% of prepandemic peak) and did not revert to prepandemic levels in subsequent phases, even after vaccine availability. Percutaneous coronary intervention and coronary artery bypass grafting volumes declined proportionally. Compared with the prepandemic period, patients with NSTEMI experienced higher 30-day mortality during Phases 2 and 3, even after adjustment for COVID-19-positive status, demographics, baseline comorbidities, and receipt of procedural treatment (adjusted odds ratio for Phases 2 and 3 combined, 1.26 [95% CI, 1.13-1.43], P<0.01). Patients receiving Veterans Affairs-paid community care had a higher adjusted risk of 30-day mortality compared with those at Veterans Affairs hospitals across all 6 pandemic phases. Conclusions Higher mortality after NSTEMI occurred during the initial spread and first peak of the pandemic but resolved before the second, higher peak-suggesting effective adaptation of care delivery but a costly delay to implementation. Investigation into the vulnerabilities of the early pandemic spread are vital to informing future resource-constrained practices.

Healthcare costs and use before and after opioid overdose in Veterans Health Administration patients with opioid use disorder.

AIMS: To compare healthcare costs and use between United States (US) Veterans Health Administration (VHA) patients with opioid use disorder (OUD) who experienced an opioid overdose (OD cohort) and patients with OUD who did not experience an opioid overdose (non-OD cohort). DESIGN: This is a retrospective cohort study of administrative and clinical data. SETTING: The largest integrated national health-care system is the US Veterans Health Administration's healthcare systems. PARTICIPANTS: We included VHA patients diagnosed with OUD from October 1, 2017 through September 30, 2018. We identified the index date of overdose for patients who had an overdose. Our control group, which included patients with OUD who did not have an overdose, was randomly assigned an index date. A total of 66 513 patients with OUD were included for analysis (OD cohort: n = 1413; non-OD cohort: n = 65 100). MEASUREMENTS: Monthly adjusted healthcare-related costs and use in the year before and after the index date. We used generalized estimating equation models to compare patients with an opioid overdose and controls in a difference-in-differences framework. FINDINGS: Compared with the non-OD cohort, an opioid overdose was associated with an increase of $16 890 [95% confidence interval (CI) = $15 611-18 169; P < 0.001] in healthcare costs for an estimated $23.9 million in direct costs to VHA (95% CI = $22.1 million, $25.7 million) within the 30 days following overdose after adjusting for baseline characteristics. Inpatient costs ($13 515; 95% CI = $12 378-14 652; P < 0.001) reflected most of this increase. Inpatient days (+6.15 days; 95% CI, = 5.33-6.97; P < 0.001), inpatient admissions (+1.01 admissions; 95% CI = 0.93-1.10; P < 0.001) and outpatient visits (+1.59 visits; 95% CI = 1.34-1.84; P < 0.001) also increased in the month after opioid overdose. Within the overdose cohort, healthcare costs and use remained higher in the year after overdose compared with pre-overdose trends. CONCLUSIONS: The US Veterans Health Administration patients with opioid use disorder (OUD) who have experienced an opioid overdose have increased healthcare costs and use that remain significantly higher in the month and continuing through the year after overdose than OUD patients who have not experienced an overdose.

Adapting a Telephone-Based, Dyadic Self-management Program to Be Delivered Over the Web: Methodology and Usability Testing.

BACKGROUND: The COVID-19 pandemic has amplified the need for web-based behavioral interventions to support individuals who are diagnosed with chronic conditions and their informal caregivers. However, most interventions focus on patient outcomes. Dyadic technology-enabled interventions that simultaneously improve outcomes for patients and caregivers are needed. OBJECTIVE: This study aimed to describe the methodology used to adapt a telephone-based, facilitated, and dyadic self-management program called Self-care Using Collaborative Coping Enhancement in Diseases (SUCCEED) into a self-guided, web-based version (web-SUCCEED) and to conduct usability testing for web-SUCCEED. METHODS: We developed web-SUCCEED in 6 steps: ideation-determine the intervention content areas; prototyping-develop the wireframes, illustrating the look and feel of the website; prototype refinement via feedback from focus groups; finalizing the module content; programming web-SUCCEED; and usability testing. A diverse team of stakeholders including content experts, web designers, patients, and caregivers provided input at various stages of development. Costs, including full-time equivalent employee, were summarized. RESULTS: At the ideation stage, we determined the content of web-SUCCEED based on feedback from the program's original pilot study. At the prototyping stage, the principal investigator and web designers iteratively developed prototypes that included inclusive design elements (eg, large font size). Feedback about these prototypes was elicited through 2 focus groups of veterans with chronic conditions (n=13). Rapid thematic analysis identified two themes: (1) web-based interventions can be useful for many but should include ways to connect with other users and (2) prototypes were sufficient to elicit feedback about the esthetics, but a live website allowing for continual feedback and updating would be better. Focus group feedback was incorporated into building a functional website. In parallel, the content experts worked in small groups to adapt SUCCEED's content, so that it could be delivered in a didactic, self-guided format. Usability testing was completed by veterans (8/16, 50%) and caregivers (8/16, 50%). Veterans and caregivers gave web-SUCCEED high usability scores, noting that it was easy to understand, easy to use, and not overly burdensome. Notable negative feedback included "slightly agreeing" that the site was confusing and awkward. All veterans (8/8, 100%) agreed that they would choose this type of program in the future to access an intervention that aims to improve their health. Developing and maintaining the software and hosting together cost approximately US $100,000, excluding salary and fringe benefits for project personnel (steps 1-3: US $25,000; steps 4-6: US $75,000). CONCLUSIONS: Adapting an existing, facilitated self-management support program for delivery via the web is feasible, and such programs can remotely deliver content. Input from a multidisciplinary team of experts and stakeholders can ensure the program's success. Those interested in adapting programs should have a realistic estimate of the budget and staffing requirements.

Excess Mortality Among Patients in the Veterans Affairs Health System Compared With the Overall US Population During the First Year of the COVID-19 Pandemic.

Importance: During the first year of the COVID-19 pandemic, there was a substantial increase in the rate of death in the United States. It is unclear whether those who had access to comprehensive medical care through the Department of Veterans Affairs (VA) health care system had different death rates compared with the overall US population. Objective: To quantify and compare the increase in death rates during the first year of the COVID-19 pandemic between individuals who received comprehensive medical care through the VA health care system and those in the general US population. Design, Setting, and Participants: This cohort study compared 10.9 million enrollees in the VA, including 6.8 million active users of VA health care (those with a visit in the last 2 years), with the general population of the US, with deaths occurring from January 1, 2014, to December 31, 2020. Statistical analysis was conducted from May 17, 2021, to March 15, 2023. Main Outcomes and Measures: Changes in rates of death from any cause during the COVID-19 pandemic in 2020 compared with previous years. Changes in all-cause death rates by quarter were stratified by age, sex, race and ethnicity, and region, based on individual-level data. Multilevel regression models were fit in a bayesian setting. Standardized rates were used for comparison between populations. Results: There were 10.9 million enrollees in the VA health care system and 6.8 million active users. The demographic characteristics of the VA populations were predominantly male (>85% in the VA health care system vs 49% in the general US population), older (mean [SD], 61.0 [18.2] years in the VA health care system vs 39.0 [23.1] years in the US population), and had a larger proportion of patients who were White (73% in the VA health care system vs 61% in the US population) or Black (17% in the VA health care system vs 13% in the US population). Increases in death rates were apparent across all of the adult age groups (≥25 years) in both the VA populations and the general US population. Across all of 2020, the relative increase in death rates compared with expected values was similar for VA enrollees (risk ratio [RR], 1.20 [95% CI, 1.14-1.29]), VA active users (RR, 1.19 [95% CI, 1.14-1.26]), and the general US population (RR, 1.20 [95% CI, 1.17-1.22]). Because the prepandemic standardized mortality rates were higher in the VA populations prior to the pandemic, the absolute rates of excess mortality were higher in the VA populations. Conclusions and Relevance: In this cohort study, a comparison of excess deaths between populations suggests that active users of the VA health system had similar relative increases in mortality compared with the general US population during the first 10 months of the COVID-19 pandemic.

Factors influencing pediatric transfusion: A complex decision impacting quality of care.

BACKGROUND: The risks of red blood cell transfusion may outweigh the benefits for many patients in pediatric intensive care units (PICUs), but guidelines from the Transfusion and Anemia eXpertise Initiative (TAXI) have not been consistently adopted. We sought to identify factors that influenced transfusion decision-making in PICUs to explore potential barriers and facilitators to implementing the guidelines. STUDY DESIGN AND METHODS: A total of 50 ICU providers working in eight US ICUs of different types (non-cardiac PICUs, cardiovascular ICUs, combined units) and variable sizes (11-32 beds) completed semi-structured interviews. Providers included ICU attendings and trainees, nurse practitioners, nurses, and subspecialty physicians. Interviews examined factors that influenced transfusion decisions, transfusion practices, and provider beliefs. Qualitative analysis utilized a Framework Approach. Summarized data was compared between provider roles and units with consideration to identify patterns and unique informative statements. RESULTS: Providers cited clinical, physiologic, anatomic, and logistic factors they considered in making transfusion decisions. Improving oxygen carrying capacity, hemodynamics and perfusion, respiratory function, volume deficits, and correcting laboratory values were among the reasons given for transfusion. Other sought-after benefits included alleviating symptoms of anemia, improving ICU throughput, and decreasing blood waste. Providers in different roles approached transfusion decisions differently, with the largest differences noted between nurses and subspecialists as compared with other ICU providers. While ICU attendings most often made the decision to transfuse, all providers influenced the decision-making. DISCUSSION: Implementation of transfusion guidelines requires multi-professional approaches that emphasize the known risks of transfusion, its limited benefits, and highlight evidence around the safety and benefit of restrictive approaches.

Integrating Patient and Expert Perspectives to Conceptualize High-Quality Palliative Cancer Care for Symptoms in the US Veterans Health Administration: A Qualitative Study.

Quality measurement is typically the domain of clinical experts and health system leaders; patient/caregiver perspectives are rarely solicited. We aimed to describe and integrate clinician and patient/caregiver conceptualizations of high-quality palliative symptom care for patients receiving care for advanced cancer within the US Veterans Health Administration in the context of existing quality measures. We conducted a secondary qualitative analysis of transcripts from prioritization discussions of process quality measures relevant to cancer palliative care. These discussions occurred during 2 modified RAND-UCLA appropriateness panels: a panel of 10 palliative care clinical expert stakeholders (7 physicians, 2 nurses, 1 social worker) and a panel of 9 patients/caregivers with cancer experience. Discussions were recorded, transcribed, and independently double-coded using an a priori logical framework. Content analysis was used to identify subthemes within codes and axial coding was used to identify crosscutting themes. Patients/caregivers and clinical experts contributed important perspectives to 3 crosscutting themes. First, proactive elicitation of symptoms is critical. Patients/caregivers especially emphasized importance of comprehensive and proactive screening and assessment, especially for pain and mental health. Second, screening and assessment alone is not enough; information elicited from patients must inform care. Measuring screening/assessment and management care processes separately has important limitations. Lastly, high-quality symptom management can be broadly defined if it is patient-centered; high-quality care takes an individualized approach and might include non-medical or non-pharmacological symptom management. Integrating the perspectives of clinical experts and patients/caregivers is critical for health systems to consider as they design and implement quality measures for palliative cancer care.