Identifying Optimal Acute Care Comparators to Inform the Evaluation of an Advanced Care at Home Pilot Program.

BACKGROUND: Hospital at Home (H@H) programs-which seek to deliver acute care within a patient's home-have become more prevalent over time. However, existing literature exhibits heterogeneity in program structure, evaluation design, and target population size, making it difficult to draw generalizable conclusions to inform future H@H program design. OBJECTIVE: The objective of this work was to develop a quality improvement evaluation strategy for a H@H program-the Kaiser Permanente Advanced Care at Home (KPACAH) program in Northern California-leveraging electronic health record data, chart review, and patient surveys to compare KPACAH patients with inpatients in traditional hospital settings. METHODS: The authors developed a 3-step recruitment workflow that used electronic health record filtering tools to generate a daily list of potential comparators, a manual chart review of potentially eligible comparator patients to assess individual clinical and social criteria, and a phone interview with patients to affirm eligibility and interest from potential comparator patients. RESULTS: This workflow successfully identified and enrolled a population of 446 comparator patients in a 5-month period who exhibited similar demographics, reasons for hospitalization, comorbidity burden, and utilization measures to patients enrolled in the KPACAH program. CONCLUSION: These initial findings provide promise for a workflow that can facilitate the identification of similar inpatients hospitalized at traditional brick and mortar facilities to enhance outcomes evaluations for the H@H programs, as well as to identify the potential volume of enrollees as the program expands.

Disparities in disaster healthcare: A review of past disasters.

OBJECTIVE: To review the literature on the effects seen after disaster on those with poor social determinants of health (SDOH) and individual social needs. DESIGN: The Disaster Preparedness and Response Committee of the American College of Emergency Physicians (ACEP) formed a work group to study healthcare disparities seen in disaster. This group was composed of six physicians on the committee, all of whom have extensive background in disaster medicine and the chair of the committee. A systematic literature review regarding past disasters and all the healthcare disparities seen was undertaken with the goal of organizing this information in one broad concise document looking at multiple disasters over history. The group reviewed multiple documents regarding SDOH and individual social needs for a complete understanding of these factors. Then, a topic list of healthcare disparities resulting from these factors was composed. This list was then filled out with subtopics falling under the header topics. Each member of the workgroup took one of these topics of healthcare disparity seen in disasters and completed a literature search. The databases reviewed include PubMed Central, Google Scholar, and Medline. The terms queried were disaster, healthcare disparities, disaster healthcare disparities, healthcare disparities associated with disasters, SDOH and disaster, special populations and disaster effects, and vulnerable populations and disaster effects. Each author chose articles they felt were most representative and demonstrative of the healthcare disparities seen in past disasters. These social determinant factors and individual social needs were then cross referenced in relation to past disasters for both their causes and the effect they had on various populations after disaster. This was presented to the ACEP board as a committee report. RESULTS: All the SDOH and individual social needs showed significant negative effects for the populations when combined with a disaster event. These SDOH cut across age, race, and gender affecting a wide swath of people. Previous disaster planning either did not plan or under planned for these marginalized populations during disaster events. CONCLUSIONS: Disparities in healthcare are a pervasive problem that effects many different groups. Disasters magnify and more fully expose these healthcare disparities. We have explored the healthcare disparities with past disasters. These disparities, although common, can be mitigated. The recognition of these poor determinants of health can lead to better and more comprehensive disaster planning for future disasters. Subsequent research is needed to explore these healthcare disparities exacerbated by disasters and to find methods for their mitigation.

Diferencias en la atención sostenida en jóvenes universitarios con distintos niveles de uso de smartphone / Differences in sustained attention in young university students with different levels of smartphone use

Resumen El uso constante de los dispositivos móviles está generando nuevos fenómenos de comportamiento. En años recientes, se ha puesto énfasis en los cambios cognitivos que se podrían generar en los jóvenes que hacen uso excesivo de estos dispositivos. El objetivo del trabajo fue conocer las diferencias en la atención sostenida en jóvenes universitarios asociadas a distintos niveles de uso del teléfono inteligente. Se obtuvo una muestra de 94 adultos, 34 hombres y 60 mujeres de 18 a 23 años (M = 19.34, DE = 1.09) alumnos de la escuela superior de Actopan, Hidalgo-México. Se aplicó la Escala de Dependencia y Adicción al Smartphone EDAS (Aranda-López et al., 2017) y una prueba computarizada de ejecución continua (CPT) Test of Atenttional Vigilance (TOAV; Mueller y Pipper, 2014). Se realizó un ANOVA de una vía, en el que la variable independiente fue el nivel de uso del teléfono inteligente (sin dependencia, dependencia y adicción) y la variable dependiente fueron las puntuaciones obtenidas en el TOAV. Se observó que existen diferencias significativas a nivel estadístico en lo relativo a errores de omisión de la segunda mitad de la prueba (p = .005); las diferencias fueron entre los grupos de sin dependencia-dependencia (p = .010) y sin dependencia-adicción (p = .024). Acorde a los hallazgos del presente estudio, existen diferencias en el proceso de atención sostenida entre usuarios con diferentes niveles de uso del teléfono inteligente; los estudiantes con niveles de dependencia y adicción enfrentan dificultades en la atención sostenida cuando la tarea se prolonga y aumenta la demanda cognitiva.

Abstract The constant use of mobile devices changed our lives dramatically during the past years and its usage increased over the years. Smartphone use is associated with isolation and interpersonal problems; its overuse can cause cognitive problems too (Matar Boumosleh & Jaalouk, 2017). Cognitive problems associated with smartphones in young people are reduction of sustained attention and working memory. Findings have been reported in which younger populations show deterioration in different components of care, highlighting the difficulty of walking and using the smartphone at the same time (Prupetkaew et al., 2019). It has been reported that the impulsivity associated with use of smartphone in silent mode interferes in memory tests unlike when it is in off mode in young populations (Canale et al., 2019). It is necessary to evaluate the effects of using a smartphone on young people because it is a population that uses it constantly to develop in work, academic, sports, and even socializing activities. The aim of this paper was to find out the differences in sustained attention in young university students with different levels of smartphone use. A sample of 94 adults, 34 men and 60 women between the ages of 18 and 23 (M = 19.34, SD = 1.09), who were students of the higher school of Actopan, Hidalgo-Mexico. The EDAS -Smartphone Dependency and Addiction Scale- was applied (Aranda-López et al., 2017). For the evaluation of attention, a Computerized Continuous Running Test (CPT), Test of Attentional Vigilance (TOAV) was applied using the Psychology Experimental Building Language PEBL-2 platform (Mueller & Pipper, 2014). The inclusion criteria were that the participants were between 18-23 years old, right-handed, with normal and/or corrected vision. They were excluded from the investigation if they had a history of psychiatric and/or neurological diseases, learning difficulties, chronic alcohol and/or drug use. A one-way ANOVA was performed, where the independent variable was the level of smartphone use (no dependence, dependence and addiction) and the dependent variable was the scores obtained in the TOAV. It was observed that there are statistically significant differences in the errors of omission of the second half of the test (p = .05), the differences were found between the groups of no dependence-dependence (p =.10) and without dependence-addiction (p = .24). The results showed that there are differences in the execution of a neuropsychological task, regarding the omission errors of the second part of the test. These differences could suggest that the level of sustained attention is diminished in the participants of the dependency and addiction group at the end of the task. On the other hand, it is also concluded that students with levels of dependence and smartphone addiction face attention difficulties when the task is longer and cognitive demand increases. This type of data must be analyzed taking into consideration variables such as sex, socioeconomic level, age, profile of use, quality of sleep, level of physical activity, among others.

The Kaiser Permanente Northern California Advance Alert Monitor Program: An Automated Early Warning System for Adults at Risk for In-Hospital Clinical Deterioration.

BACKGROUND: In-hospital deterioration among ward patients is associated with substantially increased adverse outcome rates. In 2013 Kaiser Permanente Northern California (KPNC) developed and implemented a predictive analytics-driven program, Advance Alert Monitor (AAM), to improve early detection and intervention for in-hospital deterioration. The AAM predictive model is designed to give clinicians 12 hours of lead time before clinical deterioration, permitting early detection and a patient goals-concordant response to prevent worsening. DESIGN OF THE AAM INTERVENTION: Across the 21 hospitals of the KPNC integrated health care delivery system, AAM analyzes electronic health record (EHR) data for patients in medical/surgical and telemetry units 24 hours a day, 7 days a week. Patients identified as high risk by the AAM algorithm trigger an alert for a regional team of experienced critical care virtual quality nurse consultants (VQNCs), who then cascade validated, actionable information to rapid response team (RRT) nurses at local hospitals. RRT nurses conduct bedside assessments of at-risk patients and formulate interdisciplinary clinical responses with hospital-based physicians, bedside nurses, and supportive care teams to ensure a well-defined escalation plan that includes clarification of the patients' goals of care. SUCCESS OF THE INTERVENTION: Since 2019 the AAM program has been implemented at all 21 KPNC hospitals. The use of predictive modeling embedded within the EHR to identify high-risk patients has produced the standardization of monitoring workflows, clinical rescue protocols, and coordination to ensure that care is consistent with patients' individual goals of care. An evaluation of the program, using a staggered deployment sequence over 19 hospitals, demonstrates that the AAM program is associated with statistically significant decreases in mortality (9.8% vs. 14.4%), hospital length of stay, and ICU length of stay. Statistical analyses estimated that more than 500 deaths were prevented each year with the AAM program. LESSONS LEARNED: Unlocking the potential of predictive modeling in the EHR is the first step toward realizing the promise of artificial intelligence/machine learning (AI/ML) to improve health outcomes. The AAM program leveraged predictive analytics to produce highly reliable care by identifying at-risk patients, preventing deterioration, and reducing adverse outcomes and can be used as a model for how clinical decision support and inpatient population management can effectively improve care.

What Do We Do After the Pilot Is Done? Implementation of a Hospital Early Warning System at Scale.

BACKGROUND: Adults who deteriorate outside the ICU have high mortality. Most rapid response systems (RRSs) have employed manual detection processes that rapid response teams (RRTs) use to identify patients at risk. This project piloted the use of an automated early warning system (EWS), based on a very large database, that provides RRTs with 12 hours lead time to mount a response. Results from a 2-hospital pilot were encouraging, so leadership decided to deploy the Advance Alert Monitor (AAM) program in 19 more hospitals. CHALLENGE: How can one deploy an RRS using an automated EWS at scale? SOLUTION: EWS displays were removed from frontline clinicians' hospital electronic dashboards, and a Virtual Quality Team (VQT) RN was interposed between the EWS and the RRT. VQT RNs monitor the EWS remotely-when alerts are issued, they conduct a preliminary chart review and contact hospital RRT RNs. VQT and RRT RNs review the cases jointly. The RRT RNs then consult with hospitalists regarding clinical rescue and/or palliative care workflows. Subsequently, VQT RNs monitor patient charts, ensuring adherence to RRS practice standards. To enable this process, the project team developed a governance structure, clinical workflows, palliative care workflows, and documentation standards. RESULTS: The AAM Program now functions in 21 Kaiser Permanente Northern California hospitals. VQT RNs monitor EWS alerts 24 hours a day, 7 days a week. The AAM Program handles ∼16,000 alerts per year. Its implementation has resulted in standardization of RRT staffing, clinical rescue workflows, and in-hospital palliative care.

Enhanced Recovery After Surgery to Change Process Measures and Reduce Opioid Use After Cesarean Delivery: A Quality Improvement Initiative.

OBJECTIVE: To evaluate implementation of an enhanced recovery after surgery (ERAS) program for patients undergoing elective cesarean delivery by comparing opioid exposure, multimodal analgesia use, and other process and outcome measures before and after implementation. METHODS: An ERAS program was implemented among patients undergoing elective cesarean delivery in a large integrated health care delivery system. We conducted a pre-post study of ERAS implementation to compare changes in process and outcome measures during the 12 months before and 12 months after implementation. RESULTS: The study included 4,689 patients who underwent an elective cesarean delivery in the 12 months before (pilot sites: March 1, 2015-February 29, 2016, all other sites: October 1, 2015-September 30, 2016), and 4,624 patients in the 12 months after (pilot sites: April 1, 2016-March 31, 2017, all other sites: November 1, 2016-October 31, 2017) ERAS program implementation. After ERAS implementation mean inpatient opioid exposure (average daily morphine equivalents) decreased from 10.7 equivalents (95% CI 10.2-11.3) to 5.4 equivalents (95% CI 4.8-5.9) controlling for age, race-ethnicity, prepregnancy body mass index, patient reported pain score, and medical center. The use of multimodal analgesia (ie, acetaminophen and neuraxial anesthesia) increased from 9.7% to 88.8%, the adjusted risk ratio (RR) for meeting multimodal analgesic goals was 9.13 (RR comparing post-ERAS with pre-ERAS; 95% CI 8.35-10.0) and the proportion of time patients reported acceptable pain scores increased from 82.1% to 86.4% (P<.001). Outpatient opioids dispensed at hospital discharge decreased from 85.9% to 82.2% post-ERAS (P<.001) and the average number of dispensed pills decreased from 38 to 26 (P<.001). The hours to first postsurgical ambulation decreased by 2.7 hours (95% CI -3.1 to -2.4) and the hours to first postsurgical solid intake decreased by 11.1 hours (95% CI -11.5 to -10.7). There were no significant changes in hospital length of stay, surgical site infections, hospital readmissions, or breastfeeding rates. CONCLUSIONS: Implementation of an ERAS program in patients undergoing elective cesarean delivery was associated with a reduction in opioid inpatient and outpatient exposure and with changes in surgical process measures of care without worsened surgical outcomes.

The Kaiser Permanente Northern California Enhanced Recovery After Surgery Program: Design, Development, and Implementation.

Complications are common after surgery, highlighting the need for innovations that reduce postsurgical morbidity and mortality. In this report, we describe the design, development, and implementation of an Enhanced Recovery After Surgery program in the Kaiser Permanente Northern California integrated health care delivery system. This program was implemented and disseminated in 2014, targeting patients who underwent elective colorectal resection and those who underwent emergent hip fracture repair across 20 Medical Centers. The program leveraged multidisciplinary and broad-based leadership, high-quality data and analytic infrastructure, patient-centered education, and regional-local mentorship alignment. This program has already had an impact on more than 17,000 patients in Northern California. It is now in its fourth phase of planning and implementation, expanding Enhanced Recovery pathways to all surgical patients across Kaiser Permanente Northern California.

Ready or not: does household preparedness prevent absenteeism among emergency department staff during a disaster?

INTRODUCTION: During major disasters, hospitals experience varied levels of absenteeism among healthcare workers (HCWs) in the immediate response period. Loss of critical hospital personnel, including Emergency Department (ED) staff, during this time can negatively impact a facility's ability to effectively treat large numbers of ill and injured patients. Prior studies have examined factors contributing to HCW ability and willingness to report for duty during a disaster. The purpose of this study was to determine if the degree of readiness of ED personnel, as measured by household preparedness, is associated with predicted likelihood of reporting for duty. Additionally, the authors sought to elucidate other factors associated with absenteeism among ED staff during a disaster. METHODS: ED staff of five hospitals participated in this survey-based study, answering questions regarding demographic information, past disaster experience, household disaster preparedness (using a novel,15-point scale), and likelihood of reporting to work during various categories of disaster. The primary outcome was personal predicted likelihood of reporting for duty following a disaster. RESULTS: A total of 399 subjects participated in the study. ED staffs were most likely to report for duty in the setting of an earthquake (95 percent) or other natural disaster, followed by an epidemic (90 percent) and were less likely to report for work during a biological, chemical, or a nuclear event (63 percent). Degree of household preparedness was determined to have no association with an ED HCW's predicted likelihood of reporting for duty. Factors associated with predicted absenteeism varied based on type of disaster and included having dependents in the home, female gender, past disaster relief experience, having a spouse or domestic partner, and not owning pets. Having dependents in the home was associated with predicted absenteeism for all disaster types (OR 0.30-0.66). However, when stratified by gender, the presence of dependents at home was only a significantly associated with predicted absenteeism among women as opposed to men (OR 0.07-0.59 versus OR 0.41-1.02). DISCUSSION: Personal household preparedness, while an admirable goal, appears to have no effect on predicted absenteeism among ED staff following a disaster. Having responsibilities for dependents is the most consistent factor associated with predicted absenteeism among female staff. Hospital and ED disaster planners should consider focusing preparedness efforts less toward household preparedness for staff and instead concentrate on addressing dependent care needs in addition to professional preparedness.

New developments in high-throughput resequencing and variation detection using high density microarrays.

We developed a high-throughput method for resequencing for single nucleotide polymorphism (SNP) discovery using high-density microarrays. Over the two-year course of this study a number of improvements in sample preparation methods, hybridization assay, array handling, and analysis method were developed and implemented. DNA from 40 unrelated individuals of three different ethnic origins was amplified, labeled, and hybridized to arrays designed with probes representing genomic, coding, and regulatory regions. Protocol improvements including the use of long PCR and semi-automation reduced labeling and fragmentation costs by 33%. Automation improvements include the development of a scanner autoloader for arrays, a faster array wash station, and a linked laboratory tracking and data management system. Validation of a smaller feature size, 20 x 24 microns, allowed the simultaneous screening of 30-kb sense and 30-kb antisense DNA on each microarray, increasing throughput to 1.4 Mb per day per two laboratory personnel. More than 15,000 SNPs were identified in 8.3 Mb of the human genome using high-density resequencing and variation detection arrays (microarrays).