Staffing Models Research Articles

Mobilizing human resources

The COVID-19 pandemic has confronted health systems with extraordinary changes in demand for health services, which magnified underlying disparities in the health workforce. Initial health workforce capacities were critical, as health systems only have two options to increase workforce level: increasing capacity among the existing workforce or mobilising/recruiting additional personnel. Workforce capacity became a limiting factor in an effective pandemic response, particularly in countries with acute workforce shortages. Countries in Europe have pursued various strategies to rapidly surge the numbers of health workers and using their existing human resources differently, including by introducing or expanding on alternative and flexible approaches such as task shifting and other skill-mix changes. The pressure to provide services across the continuum of care, from prevention and vaccination to specialist and inpatient care, led several countries to re-evaluate the distribution of tasks among the health workforce. In the best cases, multidisciplinary team-based staffing models combined the skills of multiple health professionals, enabling provision of the right care at the right time. This presentation will provide an overview of strategies to increase capacity for surges in human resources requirements, as well as supporting measures that help recognise the key role of health workers in the pandemic response. A brief overview of key metrics to assess resilience in the area of human resources will also be provided.

Nurses’ Perceptions of the Buddy Staffing Model: Implications for Nurse Leaders

During the past year, nurse leaders faced unprecedented staffing challenges due to the surge of patients from the pandemic resulting in the implementation of various staffing models. This quality improvement project examines the nurses’ perceptions of the benefits of a buddy staffing model (BSM). The potential benefits of the BSM include promoting efficiency, maintaining patient safety, cultivating teamwork, providing fundamental nursing care, and feeling supported by leadership. Understanding the nurses' perceptions of buddy models provides critical information for nurse leaders as we move forward with future surges and face the implications of the “Great Resignation, 2021.”

Focusing hospitalist roles on either admitting or rounding facilitates unit-based assignment and is associated with improved discharge efficiency

ABSTRACT Background Unit-based teams may improve care delivery for hospitalized patients but can be challenging to implement broadly across all acute care units in a hospital. Objective To determine the effect of a Lean-guided transition to hospital-wide unit-based assignment on care delivery outcomes. Design, setting, and participants The study was a retrospective time-series with primary outcomes of discharge efficiency, 30-day readmissions, and length of stay, performed at a 336-bed tertiary academic referral hospital in the Pacific Northwest with approximately 17,000 admissions annually. Intervention Implementation of a Lean-guided quality improvement intervention included division of hospitalist duties into ‘admitters’ and ‘rounders,’ with simulated patient flow exercises to determine the optimal staffing model. Main outcomes and measures Discharge efficiency (number of patients discharged by hospitalists divided by the number of hospitalist patient encounter days per month) and 30-day readmissions were compared using the t-test or chi-square, and length of stay was analyzed in a multivariate time-series regression model. Results The intervention was associated with a significant improvement in discharge efficiency, by 0.014 (from 0.168 to 0.181) discharges/encounter (95% CI = 0.024, 0.004), p = 0.009. Mean length of stay decreased by 0.98 days (95% CI 0.50, 1.47) after adjustment for patient age, patient type (medical versus surgical), critical care admissions, and discharge disposition, without a corresponding change in 30-day readmission rate (12.2% (1948/15,902) pre-intervention to 11.7% (397/3379) post-intervention (p = 0.42)). Conclusions Dividing hospitalist roles into admitters and rounders enabled implementation of unit-based teams across the hospital, with corresponding improvements in discharge efficiency and length of stay.

Analyzing supply and demand on a general internal medicine ward: a cross-sectional study.

Background:The capacity of general internal medicine (GIM) clinical teaching units has been strained by decreasing resident supply and increasing patient demand. The objective of our study was to compare the number of residents (supply) with the volume and duration of patient care activities (demand) to identify inefficiency.Methods:Using the most recently available data from an academic teaching hospital in Toronto, Ontario, we identified each occurrence of a set of patient care activities that took place on the clinical teaching unit from 2015 to 2019. We completed a descriptive analysis of the frequencies of these activities and how the frequencies varied by hour, day, week, month and year. Patient care activities included admissions, rounds, responding to pages, meeting with patients and their families, patient transfers, discharges and responding to cardiac arrests. The estimated time to complete each task was based on the available data in our electronic medical record system and interviews with GIM physicians and trainees. To calculate resident utilization, the person-hours of patient care tasks was divided by the person-hours of resident supply. Resident utilization was computed for 3 scenarios corresponding to varying levels of resident absenteeism.Results:During the study period, there were 14 581 consultations to GIM from the emergency department. Patient volumes tended to be highest during January and lowest during May and June, and highest on Monday morning and lowest on Friday night. Daily admissions to hospital from the emergency department were higher on weekdays than on weekends, and hourly admissions peaked at 8 am and between 3 pm and 1 am. Weekday resident utilization was generally highest between 8 am and 2 pm, and lowest between 1 am and 8 am. In a scenario in which all residents were present apart from those who were post-call, resident utilization generally never exceeded 100%; in scenarios in which at least 1 resident was absent owing to illness or vacation, it was common for resident utilization to approach or exceed 100%, particularly during daytime working hours.Interpretation:Analyzing supply and demand on a GIM ward has allowed us to identify periods when supply and demand are not aligned and to demonstrate empirically the vulnerability of current staffing models. These data have the potential to inform and optimize scheduling on an internal medicine ward.

Cross-Sectional Comparison of Treatment Provided Under the Clinical, Integrated, and Partnership Staffing Models for Community-Based Residential Mental Health Rehabilitation.

In Queensland (Australia), community-based residential mental health rehabilitation services have three distinct staffing profiles. The traditional 'clinical' staffing model has nursing staff occupying most staff roles. The 'partnership' approach involves collaboration between the health service and a Non-Government Organisation. Under the 'integrated' staffing approach, Peer Support Workers reflect the majority staffing component. This study compares the treatment received by consumers (N = 172) under these staffing models using cross-sectional administrative data. Staffing models were generally comparable on demographic, diagnostic, and symptomatic/impairment measures. However, statistically significant differences were present on a range of treatment variables. Differences mainly occurred between the clinical and integrated approaches, with the integrated staffing model having lower rates of involuntary treatment, antipsychotic polypharmacy, depot use, and chlorpromazine dose equivalence levels. These findings indicate the need to carefully examine the impact of staffing configuration on rehabilitation processes to understand whether differences in approaches are likely to impact rehabilitation outcomes.

Managing critical care during COVID-19 pandemic: The experience of an ICU of a tertiary care hospital

BackgroundThe COVID-19 pandemic has strained ICUs worldwide. To learn from our experience, we described the critical care response to the outbreak. MethodsThis is a case study of the response of the Intensive Care Department (75-bed capacity) at a tertiary-care hospital to COVID-19 pandemic, which resulted in a high number of critically ill patients. ResultsBetween March 1 and July 31, 2020, 822 patients were admitted to the adult non-cardiac ICUs with suspected (72%)/confirmed (38%) COVID-19. At the peak of the surge, 125 critically ill patients with COVID-19 were managed on single day. To accommodate these numbers, the bed capacity of 4 ICUs was increased internally from 58 to 71 beds (+40%) by cohorting 2 patients/room in selected rooms; forty additional ICUs beds were created in 2 general wards; one cardiac ICU was converted to managed non-COVID-19 general ICU patients and one ward was used as a stepdown for COVID-19 patients. To manage respiratory failure, 53 new ICU ventilators, 90 helmets for non-invasive ventilation and 47 high-flow nasal cannula machines were added to the existing capacity. Dedicated medical teams cared for the COVID-19 patients to prevent cross-contamination. The nurse-to-patient and RT-to-patient ratio remained mostly 1:1 and 1:6, respectively. One-hundred-ten ward nurses were up-skilled to care for COVID-19 and other ICU patients using tiered staffing model. Daily executive rounds were conducted to identify patients for transfer and at least 10 beds were made available for new COVID-19 admissions/day. The consumption of PPE increased multiple fold compared with the period preceding the pandemic. Regular family visits were not allowed and families were updated daily by videoconferencing and phone calls. ConclusionsOur ICU response to the COVID-19 pandemic required almost doubling ICU bed capacity and changing multiple aspects of ICU workflow to be able to care for high numbers of affected patients.

Creation of Attending Weekly Night Call System Does Not Adversely Affect Productivity

Focus has increased on fatigue mitigation and limiting the number of continuous hours worked among surgical residents in recent years. Although the reported burnout rates are high, little has occurred to address similar concerns among attending surgeons. Diminished productivity has often been cited as a reason to avoid changes in traditional staffing models. In the present study, we compared the operative work relative value unit (wRVU) productivity before and after implementation of a weekly night call model in a single tertiary vascular surgery practice. In 2018, an attending call model, similar to a resident “night float,” was implemented. In this model, one surgeon would provide the weeknight call coverage (Monday through Thursday), with no clinical responsibilities during the day. A separate surgeon covered emergencies and new consultations during the day and provided backup coverage at night. The total case number and operative wRVUs were compared across the year before implementation, the year of implementation, and the year after implementation. A total of 10 attending vascular surgeons participated in the call model during the 3-year study period. Two surgeons left the practice and four were added, although the average total clinical full-time equivalent for the group (6.1) did not change significantly during the study period. The total inpatient wRVUs were stable during the year of implementation compared with the previous year and had increased significantly in the year after implementation. Similar trends were noted in the wRVU/clinical full-time equivalent and total operating room cases (Table). A paradigm shift in attending call staffing with a focus on fatigue mitigation by limiting the number of continuous hours of coverage can be undertaken in a vascular surgery practice without disruption to clinical productivity. Attending wellness should be the aim of further interventions and study.TableStudy findingsVariableBefore implementationAt implementationAfter implementationOR wRVU26,01626,62528,399RVU/FTE426543654618OR cases147417331938FTE, Full-time equivalent; OR, operating room; RVU, relative value unit; wRVU, work relative value unit. Open table in a new tab

Innovative Staffing Solutions to Nursing Shortages in Acute Mental Health Inpatient Wards

The aim of this pilot project was to investigate the perceived impact of a newly introduced therapeutic staffing model at Kent and Medway NHS and Social Care Partnership Trust (KMPT). Questionnaires were distributed to patients and staff across four wards and analysed for the purposes of getting a better understanding from patients and staff on how the model was working. Results indicate that the therapeutic staffing model was well received by patients, although staff perception was more mixed. Amongst patients, themes in staffing, therapeutic input and ward environment were identified. Amongst staff themes: shift patterns, ward duties/workload, and morale were identified. The new model appears promising, although there are some issues identified. Recommendations were made in terms of improving team cohesiveness, sense of value and professional identities.

Approaches to Psychosocial Care Within Tertiary Diabetes Care Centres in Canada: An Environmental Scan

BackgroundPatients with diabetes facing psychosocial challenges often struggle with diabetes self-management, and thereby are at risk of complications arising from hyperglycemia and hypoglycemia. Despite the Diabetes Canada Clinical Practice Guidelines’ recommendations for diabetes centres to offer psychosocial services for the screening and support of patients with psychosocial barriers, the extent to which these recommendations have been implemented in centres across Canada is unknown. MethodsTertiary adult diabetes centres affiliated with academic institutions were invited to participate in our quantitative survey (n=40). At each centre, a structured telephone interview was administered to a manager, as well as a social worker and psychologist (if employed at the centre), to inquire regarding the psychosocial services offered. ResultsThirty diabetes centres (75%) completed at least one questionnaire. Participating diabetes centres varied considerably in capacity, employing from 3 to more than 50 diabetes educators and from 0 to 20 endocrinologists. Approximately 80% of centres reported providing patients with access to psychosocial services, but only about 50% had a dedicated, internal psychosocial team. Stratified results showed centres that are the main centres affiliated with academic teaching hospitals were more likely to have embedded psychosocial services and to make referrals for psychosocial concerns when compared with satellite centres. The majority of managers reported that their staffing and funding model provided inadequate psychosocial support and that increased funding for psychosocial care was either somewhat of a priority or a high priority. ConclusionsDiabetes centres across Canada struggle to offer adequate psychosocial services; greater funding and training for staff is required to uphold the Diabetes Canada Clinical Practice Guidelines.

Implementation of social needs screening in primary care: a qualitative study using the health equity implementation framework

BackgroundScreening in primary care for unmet individual social needs (e.g., housing instability, food insecurity, unemployment, social isolation) is critical to addressing their deleterious effects on patients’ health outcomes. To our knowledge, this is the first study to apply an implementation science framework to identify implementation factors and best practices for social needs screening and response.MethodsGuided by the Health Equity Implementation Framework (HEIF), we collected qualitative data from clinicians and patients to evaluate barriers and facilitators to implementing the Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE), a standardized social needs screening and response protocol, in a federally qualified health center. Eligible patients who received the PRAPARE as a standard of care were invited to participate in semi-structured interviews. We also obtained front-line clinician perspectives in a semi-structured focus group. HEIF domains informed a directed content analysis.ResultsPatients and clinicians (i.e., case managers) reported implementation barriers and facilitators across multiple domains (e.g., clinical encounters, patient and provider factors, inner context, outer context, and societal influence). Implementation barriers included structural and policy level determinants related to resource availability, discrimination, and administrative burden. Facilitators included evidence-based clinical techniques for shared decision making (e.g., motivational interviewing), team-based staffing models, and beliefs related to alignment of the PRAPARE with patient-centered care. We found high levels of patient acceptability and opportunities for adaptation to increase equitable adoption and reach.ConclusionOur results provide practical insight into the implementation of the PRAPARE or similar social needs screening and response protocols in primary care at the individual encounter, organizational, community, and societal levels. Future research should focus on developing discrete implementation strategies to promote social needs screening and response, and associated multisector care coordination to improve health outcomes and equity for vulnerable and marginalized patient populations.

Extracorporeal Membrane Oxygenation: Opportunities for Expanding Nurses' Roles.

Extracorporeal Membrane Oxygenation: Opportunities for Expanding Nurses' Roles.

December Is Coming: A Time Trend Analysis of Monthly Variation in Adult Elective Anesthesia Caseload across Florida and Texas Locations of a Large Multistate Practice.

Anesthesia staffing models rely on predictable surgical case volumes. Previous studies have found no relationship between month of the year and surgical volume. However, seasonal events and greater use of high-deductible health insurance plans may cause U.S. patients to schedule elective surgery later in the calendar year. The hypothesis was that elective anesthesia caseloads would be higher in December than in other months. This review analyzed yearly adult case data in Florida and Texas locations of a multistate anesthesia practice from 2017 to 2019. To focus on elective caseload, the study excluded obstetric, weekend, and holiday cases. Time trend decomposition analysis was used with seasonal variation to assess differences between December and other months in daily caseload and their relationship to age and insurance subgroups. A total of 3,504,394 adult cases were included in the analyses. Overall, daily caseloads increased by 2.5 ± 0.1 cases per day across the 3-yr data set. After adjusting for time trends, the average daily December caseload in 2017 was 5,039 cases (95% CI, 4,900 to 5,177), a 20% increase over the January-to-November baseline (4,196 cases; 95% CI, 4,158 to 4,235; P < 0.0001). This increase was replicated in 2018: 5,567 cases in December (95% CI, 5,434 to 5,700) versus 4,589 cases at baseline (95% CI, 4,538 to 4,641), a 21.3% increase; and in 2019: 6,103 cases in December (95% CI, 5,871 to 6,334) versus 5,045 cases at baseline (95% CI, 4,984 to 5,107), a 21% increase (both P < 0.001). The proportion of commercially insured patients and those aged 18 to 64 yr was also higher in December than in other months. In this 3-yr retrospective analysis, it was observed that, after accounting for time trends, elective anesthesia caseloads were higher in December than in other months of the year. Proportions of commercially insured and younger patients were also higher in December. When compared to previous studies finding no increase, this pattern suggests a recent shift in elective surgical scheduling behavior.

Association of Team Structure and Chronic Disease Outcomes: Biomarker Evidence from U.S. Primary Care Clinics

Research ObjectiveThe quality of chronic disease management is highly variable and low on average. As chronic care is increasingly delivered by teams instead of solo providers, an important yet unresolved question concerns the structure of teams that tend to perform best. Optimal team structure likely depends on the mechanisms by which team‐based care may improve outcomes. Mechanisms that may favor larger, more diverse teams include more frequent patient touchpoints, complementarities between team member skill, or a higher chance of developing a strong patient‐provider relationship. Conversely, coordination costs or diffusion of responsibility may cap the optimal team size. We examine the relationship between team structure and chronic disease outcomes.Study DesignAnalysis of patient outcomes by team structure (i.e., number and types of providers seen) using rich, de‐identified EHR data. We classify teams by constructing a measure analogous to the Herfindahl–Hirschman Index (HHI) to assess provider concentration across a patient's primary care visits. Our chronic disease outcomes include biomarkers that often determine clinical decision‐making, including hemoglobin A1c, blood pressure, and LDL cholesterol, as well as process measures. First, we compare outcomes for patients who receive care from solo providers (HHI 10,000), concentrated teams (5000 &lt; HHI &lt; 10,000), balanced teams (HHI 5000), and diffuse teams (HHI &lt; 5000), adjusting for organization fixed effects and baseline disease biomarkers. Second, we identify the effect of team‐based care by studying practices that switch from solo to team‐based care, using a difference‐in‐differences design.Population Studied&gt;10 million primary care visits of &gt;1.2 million patients cared for by ~2000 providers at &gt;250 practices part of the athenahealth network from 2013–2018.Principal FindingsTaking diabetes as an example, we find that 75.7% of patients with new‐onset disease receive care from a solo provider, 12.3% from a concentrated team, 8.5% from a balanced team, and 3.5% from a diffuse team. We find that process measures differ by team structure, with solo providers ordering fewer A1c tests and prescribing fewer anti‐diabetics, compared to concentrated and diffuse teams, but not compared to balanced teams. Finally, we find that solo providers are less likely to bring their patients' diabetes under control compared to diffuse teams, but not compared to balanced or concentrated teams.ConclusionsPerformance differed by team concentration, suggesting that optimal team structure may trade off team size and frequency of visits per team member. Our study is the first, to our knowledge, to investigate the effect of different team structures on individual biomarker‐level outcomes.Implications for Policy or PracticeOur results may provide insights about the most effective team structures for increasingly consolidated primary care practices (e.g., PCMHs and ACOs). For practices seeking to adopt a team‐based care model, team structure is a potentially important factor to consider when deciding on staffing models. For payers, our results suggest that novel care coordination incentives, such as the CMS Chronic Care Management Services CPT code introduced in 2015, can be designed and further improved upon to encourage team structures that optimize the value of team‐based care.

Incorporating health systems engineering into COVID-19 vaccine planning and administration

The COVID-19 pandemic has created unique logistical challenges for vaccine transportation, inventory management, allocation and distribution at multiple levels — the federal government, states and healthcare institutions. Unpredictable weekly vaccine allocation from state health departments, changing population priorities, stringent vaccine requirements for ultracold storage, transportation, reconstitution and 2-dose administration intervals have presented challenges never seen before in the history of mass vaccination programmes, including those at Mayo Clinic. To meet the challenges, an efficient system of allocation and administration for COVID-19 vaccines was developed through collaboration with process engineering. To understand the challenges, ten health systems engineers from the Department of Management Engineering &amp; Consulting at Mayo Clinic facilitated the institution-wide COVID-19 vaccine project, collaborating closely with diverse multidisciplinary teams that included physicians, nurses, pharmacists, administrative services, information technology, human resources, scheduling operations and public affairs. The internal consultants designed tools and solutions based on systems and process engineering methodologies to solve a myriad of complex problems, including identifying priority populations, using resources efficiently and minimising vaccine waste. Tools designed included a vaccine resource playbook; dynamic staffing models based on vaccine allocation, storage, inventory and distribution processes using a hub-andspoke model; workflows and staffing models for face-to-face and drive-through vaccine administration sites; and end-of-day workflows to reduce vaccine waste. Through the collaboration, modelling and engineering, multiple sites across Mayo Clinic have implemented successful COVID-19 vaccination programmes that are efficient in resource utilisation and have minimal waste. In this paper, we share what we have learned to help other healthcare organisations prepare for future mass vaccination scenarios.

Understanding Demand and Capacity Mismatch in an Academic Emergency Department Using a Staircase Model of Provider Capacity and Staggered Shift Start Times.

Staffing and provider capacity are essential components of emergency department (ED) throughput. Patient flow is dependent on matching patient arrivals with provider capacity. Current models assume a static rate of patients per hour for providers; however, this metric has been shown to decrease throughout a shift in a pattern we describe as a staircase. We sought to analyze the demand capacity mismatch based on both a static and staircase model of resident productivity. We then suggest a new staggered staffing model that would improve flow in the ED. This was a retrospective analysis of patient demand and productivity, analyzing both static and staircase models of provider capacity. An alternative staggered shift model was then suggested, and a 2-sample t test was performed to assess if a new model reduces the amount of demand/capacity mismatch. Seventeen thousand five hundred twenty data points were analyzed over the 2-year interval, comparing the difference between actual patients placed into a treatment space at each hour and projected resident capacity based on the staircase model, using both the existing schedule and a new staggered schedule. Mean absolute values for the disparity in coverage was 2.69 (95% confidence interval 2.65-2.72) for the staircase scheduling model, and 2.14 (95% confidence interval 2.12-2.17) when staggering provider start times. The mean difference between these data sets was 0.54 (95% confidence interval 0.52-0.57; p < 0.0001). Academic EDs may find value in using a staircase model to analyze provider capacity because it is more reflective of actual capacity. EDs may benefit from visualizing their capacity curves to identify mismatches and staggering resident shifts to improve throughput and flow.

A Study of Advanced Practice Provider Staffing Models and Professional Development Opportunities at National Comprehensive Cancer Network Member Institutions.

Introduction:The National Comprehensive Cancer Network (NCCN) Best Practices Committee created an Advanced Practice Provider (APP) Workgroup to develop recommendations to support APP roles at NCCN Member Institutions.Methods:The Workgroup conducted three surveys to understand APP program structure, staffing models, and professional development opportunities at NCCN Member Institutions.Results:The total number of new and follow-up visits a 1.0 APP full-time equivalent conducts per week in shared and independent visits ranged from 11 to 97, with an average of 40 visits per week (n = 39). The type of visits APPs conduct include follow-up shared (47.2%), follow-up independent (46%), new shared (6.5%), and new independent visits (0.5%). Seventy-two percent of respondents utilize a mixed model visit type, with 15% utilizing only independent visits and 13% utilizing only shared visits (n = 39). Of the 95% of centers with APP leads, 100% indicated that leads carry administrative and clinical responsibilities (n = 20); however, results varied with respect to how this time is allocated. Professional development opportunities offered included posters, papers, and presentations (84%), leadership development (57%), research opportunities (52%), writing book chapters (19%), and other professional development activities (12%; n = 422). Twenty percent of APPs indicated that protected time to engage in development opportunities should be offered.Conclusion:As evidenced by the variability of the survey results, the field would benefit from developing standards for APPs. There is a lack of information regarding leadership structures to help support APPs, and additional research is needed. Additionally, centers should continuously assess the career-long opportunities needed to maximize the value of oncology APPs.

Population Health Innovations and Payment to Address SocialNeeds Among Patients and Communities With Diabetes.

Increasingly, health care organizations are implementing interventions to improve outcomes for patients with complex health and social needs, including diabetes, through cross-sector partnerships with nonmedical organizations. However, fee-for-service and many value-based payment systems constrain options to implement models of care that address social and medical needs in an integrated fashion. We present experiences of eight grantee organizations from the Bridging the Gap: Reducing Disparities in Diabetes Care initiative to improve diabetes outcomes by transforming primary care and addressing social needs within evolving payment models. Analysis of eight grantees through site visits, technical assistance calls, grant applications, and publicly available data from US census data (2017) and from Health Resources and Services Administration Uniform Data System Resources data (2018). Organizations represent a range of payment models, health care settings, market factors, geographies, populations, and community resources. Grantees are implementing strategies to address medical and social needs through augmented staffing models to support high-risk patients with diabetes (e.g., community health workers, behavioral health specialists), information technology innovations (e.g., software for social needs referrals), and system-wide protocols to identify high-risk populations with gaps in care. Sites identify and address social needs (e.g., food insecurity, housing), invest in human capital to support social needs referrals and coordination (e.g., embedding social service employees in clinics), and work with organizations to connect to community resources. Sites encounter challenges accessing flexible up-front funding to support infrastructure for interventions. Value-based payment mechanisms usually reward clinical performance metrics rather than measures of population health or social needs interventions. Federal, state, and private payers should support critical infrastructure to address social needs and incentivize care that addresses social needs and outcomes sensitive to social risk. Population health strategies that address medical and social needs for populations living with diabetes will need to be tailored to a range of health care organizations, geographies, populations, community partners, and market factors. Payment models should support and incentivize these strategies for sustainability.

Simulating approaches to emergency department pandemic physician staffing during COVID-19.

During pandemics, emergency departments (EDs) are challenged by the need to replace quarantined ED staff and avoid staffing EDs with nonemergency medicine (EM) trained physicians. We sought to design and examine three feasible ED staffing models intended to safely schedule EM physicians to staff three EDs within a health system during a prolonged infectious disease outbreak. We conducted simulation analyses examining the strengths and limitations of three ED clinician staffing models: two-team and three-team fixed cohort, and three-team unfixed cohort. Each model was assessed with and without immunity, and by varying infection rates. We assumed a 12-week pandemic disaster requiring a 2-week quarantine. The outcome, time to staffing shortage, was defined as depletion of available physicians in both 8- and 12-hour shift duration scenarios. All staffing models initially showed linear physician attrition with higher infection rates resulting in faster staffing shortages. The three-team fixed cohort model without immunity was not viable beyond 11 weeks. The three-team unfixed cohort model without immunity avoided staffing shortage for the duration of the pandemic up to an infection rate of 50 percent. The two-team model without immunity also avoided staffing shortage up to 30 percent infection rate. When accounting for immunity, all models behaved similarly initially but returned to adequate staffing during week 5 of the pandemic. Simulation analyses reveal fundamental tradeoffs that are critical to designing feasible pandemic disaster staffing models. Emergency physicians should test similar models based on local assumptions and capacity to ensure adequate staffing preparedness for prolonged pandemics.

Bridging the preclinical-clinical divide in response to COVID-19.

The COVID-19 pandemic posed unique challenges to educational institutions throughout the world, especially medical education programs. The usual demands of residency were heightened by increased patient volume and need for intensive care. Many hospitals, including Hackensack University Medical Center, enacted administrative changes including modifying staffing models to minimize disease exposure and remodeling hospital floors to increase the capacity of intensive care units. Many of these changes were enacted to allow for greater focus on patient care and social distancing, but they came at the expense of medical education, as traditional practices such as in-person lectures and participation in group clinical rounds were suspended to accommodate these changes. At a time when new information about SARS-CoV-2 and various treatment modalities was emerging rapidly, there was an urgent need for providing factual, relevant, concise educational materials to resident and attending physicians whose time was limited by the volume and urgency of COVID-19 patient care activities. This opened up an opportunity for students in our new medical school associated with the Hackensack Meridian Health network to provide this much needed informational resource. In March through June of 2020, first- and second-year students and medical science faculty at the Hackensack Meridian School of Medicine collaborated with resident and attending physicians at Hackensack University Medical Center via online web-conferencing. Each week, residents and attending physicians sent clinical questions to the medical school faculty that students would then research and convert into an educational presentation. The topics centered around actionable clinical queries. Students gathered relevant clinical studies and critically evaluated the methods, procedures, findings and conclusions of each report. Studies from hospitals and institutions around the world were emerging rapidly, most as preprints prior to peer-evaluation and often with a broad range of quality. Critical appraisal of data-gathering methods was essential to provide hospital staff with accurate information. Student presentations were supported by medical science faculty using standard evidence-based medicine methodology to ensure that a high-quality review had been conducted. After presentations concluded, residents and attendings posed further questions, reflected on what they were seeing clinically, and suggested new topics for discussion. This process allowed medical students to increase their skills in public speaking, critical appraising data and methodology, and presenting research in a concise, clear manner. These presentations also allowed frontline workers to receive up-to-date information in a setting of rapidly evolving knowledge of epidemiology, clinical presentation and disease management of COVID-19 patients. This innovative response to the pandemic provided a powerful opportunity to bridge medical education's preclinical-clinical divide. This example should serve as a prototype for sustained collaboration as different strains of SARS-CoV-2 emerge as a threat, as well as in post-pandemic times. This learning model can be applied to emerging diseases and is particularly useful for cutting-edge, novel research topics which significantly impact patient care.

Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach.

BackgroundHealthcare systems monitor and improve mental health treatment quality, access, continuity and satisfaction through use of population-based and efficiency-based staffing models, the former focused on staffing ratios and the latter, staff productivity. Preliminary evidence suggests that both high staffing ratios and moderate-to-high staff productivity are important for ensuring a full continuum of mental health services to indicated populations.Methods & findingsWith an information-theoretic approach, we conducted a longitudinal investigation of mental health staffing, productivity and treatment at the largest integrated healthcare system in American, the Veterans Health Administration (VHA). VHA facilities (N = 140) served as the unit of measure, with mental health treatment quality, access, continuity and satisfaction predicted by facility staffing and productivity in longitudinal mixed models. An information-theoretic approach: (a) entails the development of a comprehensive set of plausible models that are fit, ranked and weighted to quantitatively assess the relative support for each, and (b) accounts for model uncertainty while identifying best-fit model(s) that include important and exclude unimportant explanatory variables. In best-fit models, higher staffing was the strongest and most consistent predictor of better treatment quality, access, continuity and satisfaction. Higher staff productivity was often, but not always associated with better treatment quality, access, continuity and satisfaction. Results were further nuanced by differential prediction of treatment by between- and within-facility predictor effects and variable interactions.ConclusionsA population-based mental health staffing ratio and an efficiency-based productivity value are important longitudinal predictors of mental health treatment quality, access, continuity and satisfaction. Our longitudinal design and use of mixed regression models and an information-theoretic approach addresses multiple limitations of prior studies and strengthen our results. Results are discussed in terms of the provision of mental health treatment by healthcare systems, and analytical modeling of treatment quality, access, continuity and satisfaction.