Why enterprise resource planning initiatives do succeed in the long run: A case-based causal network.

Despite remarkable academic efforts, why Enterprise Resource Planning (ERP) post-implementation success occurs still remains elusive. A reason for this shortage may be the insufficient addressing of an ERP-specific interior boundary condition, i.e., the multi-stakeholder perspective, in explaining this phenomenon. This issue may entail a gap between how ERP success is supposed to occur and how ERP success may actually occur, leading to theoretical inconsistency when investigating its causal roots. Through a case-based, inductive approach, this manuscript presents an ERP success causal network that embeds the overlooked boundary condition and offers a theoretical explanation of why the most relevant observed causal relationships may occur. The results provide a deeper understanding of the ERP success causal mechanisms and informative managerial suggestions to steer ERP initiatives towards long-haul success.

Validation of a Crisis Standards of Care Model for Prioritization of Limited Resources During the Coronavirus Disease 2019 Crisis in an Urban, Safety-Net, Academic Medical Center.

OBJECTIVES: The coronavirus disease 2019 pandemic has overwhelmed healthcare resources even in wealthy nations, necessitating rationing of limited resources without previously established crisis standards of care protocols. In Massachusetts, triage guidelines were designed based on acute illness and chronic life-limiting conditions. In this study, we sought to retrospectively validate this protocol to cohorts of critically ill patients from our hospital. DESIGN: We applied our hospital-adopted guidelines, which defined severe and major chronic conditions as those associated with a greater than 50% likelihood of 1- and 5-year mortality, respectively, to a critically ill patient population. We investigated mortality for the same intervals. SETTING: An urban safety-net hospital ICU. PATIENTS: All adults hospitalized during April of 2015 and April 2019 identified through a clinical database search. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 365 admitted patients, 15.89% had one or more defined chronic life-limiting conditions. These patients had higher 1-year (46.55% vs 13.68%; p < 0.01) and 5-year (50.00% vs 17.22%; p < 0.01) mortality rates than those without underlying conditions. Irrespective of classification of disease severity, patients with metastatic cancer, congestive heart failure, end-stage renal disease, and neurodegenerative disease had greater than 50% 1-year mortality, whereas patients with chronic lung disease and cirrhosis had less than 50% 1-year mortality. Observed 1- and 5-year mortality for cirrhosis, heart failure, and metastatic cancer were more variable when subdivided into severe and major categories. CONCLUSIONS: Patients with major and severe chronic medical conditions overall had 46.55% and 50.00% mortality at 1 and 5 years, respectively. However, mortality varied between conditions. Our findings appear to support a crisis standards protocol which focuses on acute illness severity and only considers underlying conditions carrying a greater than 50% predicted likelihood of 1-year mortality. Modifications to the chronic lung disease, congestive heart failure, and cirrhosis criteria should be refined if they are to be included in future models.

Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories.

OBJECTIVES: To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. METHODS: An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. RESULTS: To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. CONCLUSIONS: The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.

A framework to support the integration of priority setting in the preparedness, alert, control and evaluation stages of a disease pandemic.

The COVID-19 pandemic, where the need-resource gap has necessitated decision makers in some contexts to ration access to life-saving interventions, has demonstrated the critical need for systematic and fair priority setting and resource allocation mechanisms. Disease outbreaks are becoming increasingly common and priority setting lessons from previous disease outbreaks could be better harnessed to inform decision making and planning for future disease outbreaks. The purpose of this paper is to discuss how priority setting and resource allocation could, ideally, be integrated into the WHO pandemic planning and preparedness framework and used to inform the COVID-19 pandemic recovery plans and plans for future outbreaks. Priority setting and resource allocation during disease outbreaks tend to evoke a process similar to the 'rule of rescue'. This results in inefficient and unfair resource allocation, negative effects on health and non-health programs and increased health inequities. Integrating priority setting and resource allocation activities throughout the four phases of the WHO emergency preparedness framework could ensure that priority setting during health emergencies is systematic, evidence informed and fair.

COVID-19 in aged care homes: a comparison of effects initial government policies had in the UK (primarily focussing on England) and Australia during the first wave.

BACKGROUND: COVID-19 pandemic has had a major impact globally, with older people living in aged care homes suffering high death rates. OBJECTIVES: We aimed to compare the impact of initial government policies on this vulnerable older population between the UK and Australia during the first wave of attack. METHODS: We searched websites of governments in the UK and Australia and media outlets. We examined the key policies including the national lockdown dates and the distribution of some important resources (personal protective equipment and testing) and the effects of these initial policies on the mortality rates in the aged care homes during the first wave of attack of COVID-19. RESULTS: We found that both countries had prioritized resources to hospitals over aged care homes during the first wave of attack. Both countries had lower priority for aged care residents in hospitals (e.g. discharging without testing for COVID-19 or discouraging admissions). However, deaths in aged care homes were 270 times higher in the UK than in Australia as on 7 May 2020 (despite UK having a population only 2.5 times larger than Australia). The lower fatality rate in Australia may have been due to the earlier lockdown strategy when the total daily cases were low in Australia (118) compared to the UK (over 1000), as well as the better community viral testing regime in Australia. CONCLUSION: In conclusion, the public health policy in Australia aimed towards earlier intervention with earlier national lockdown and more viral testing to prevent new cases. This primary prevention could have resulted in more lives being saved. In contrast, the initial policy in the UK focussed mainly on protecting resources for hospitals, and there was a delay in national lockdown intervention and lower viral testing rate, resulting in more lives lost in the aged care sector.

Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation.

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke's hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

Increased SARS-CoV-2 Testing Capacity with Pooled Saliva Samples.

We analyzed feasibility of pooling saliva samples for severe acute respiratory syndrome coronavirus 2 testing and found that sensitivity decreased according to pool size: 5 samples/pool, 7.4% reduction; 10 samples/pool, 11.1%; and 20 samples/pool, 14.8%. When virus prevalence is >2.6%, pools of 5 require fewer tests; when <0.6%, pools of 20 support screening strategies.

Assessment of Disparities Associated With a Crisis Standards of Care Resource Allocation Algorithm for Patients in 2 US Hospitals During the COVID-19 Pandemic.

Importance: Significant concern has been raised that crisis standards of care policies aimed at guiding resource allocation may be biased against people based on race/ethnicity. Objective: To evaluate whether unanticipated disparities by race or ethnicity arise from a single institution's resource allocation policy. Design, Setting, and Participants: This cohort study included adults (aged ≥18 years) who were cared for on a coronavirus disease 2019 (COVID-19) ward or in a monitored unit requiring invasive or noninvasive ventilation or high-flow nasal cannula between May 26 and July 14, 2020, at 2 academic hospitals in Miami, Florida. Exposures: Race (ie, White, Black, Asian, multiracial) and ethnicity (ie, non-Hispanic, Hispanic). Main Outcomes and Measures: The primary outcome was based on a resource allocation priority score (range, 1-8, with 1 indicating highest and 8 indicating lowest priority) that was assigned daily based on both estimated short-term (using Sequential Organ Failure Assessment score) and longer-term (using comorbidities) mortality. There were 2 coprimary outcomes: maximum and minimum score for each patient over all eligible patient-days. Standard summary statistics were used to describe the cohort, and multivariable Poisson regression was used to identify associations of race and ethnicity with each outcome. Results: The cohort consisted of 5613 patient-days of data from 1127 patients (median [interquartile range {IQR}] age, 62.7 [51.7-73.7]; 607 [53.9%] men). Of these, 711 (63.1%) were White patients, 323 (28.7%) were Black patients, 8 (0.7%) were Asian patients, and 31 (2.8%) were multiracial patients; 480 (42.6%) were non-Hispanic patients, and 611 (54.2%) were Hispanic patients. The median (IQR) maximum priority score for the cohort was 3 (1-4); the median (IQR) minimum score was 2 (1-3). After adjustment, there was no association of race with maximum priority score using White patients as the reference group (Black patients: incidence rate ratio [IRR], 1.00; 95% CI, 0.89-1.12; Asian patients: IRR, 0.95; 95% CI. 0.62-1.45; multiracial patients: IRR, 0.93; 95% CI, 0.72-1.19) or of ethnicity using non-Hispanic patients as the reference group (Hispanic patients: IRR, 0.98; 95% CI, 0.88-1.10); similarly, no association was found with minimum score for race, again with White patients as the reference group (Black patients: IRR, 1.01; 95% CI, 0.90-1.14; Asian patients: IRR, 0.96; 95% CI, 0.62-1.49; multiracial patients: IRR, 0.81; 95% CI, 0.61-1.07) or ethnicity, again with non-Hispanic patients as the reference group (Hispanic patients: IRR, 1.00; 95% CI, 0.89-1.13). Conclusions and Relevance: In this cohort study of adult patients admitted to a COVID-19 unit at 2 US hospitals, there was no association of race or ethnicity with the priority score underpinning the resource allocation policy. Despite this finding, any policy to guide altered standards of care during a crisis should be monitored to ensure equitable distribution of resources.

Prone Positioning for Severe Acute Respiratory Distress Syndrome in COVID-19 Patients by a Dedicated Team: A Safe and Pragmatic Reallocation of Medical and Surgical Work Force in Response to the Outbreak.

OBJECTIVE: The aim of this study was to define whether rapidly reallocating health care workers not experienced with PP for performing PP in ICU is feasible and safe. SUMMARY BACKGROUND DATA: In the setting of severe acute respiratory distress syndrome (ARDS), the use of prone and supine positioning procedures (PP) has been associated with improved oxygenation resulting in decreased mortality. Nevertheless, applying PP is time consuming for ICU staffs that are at risk of mental of physical exhaustion, especially with the constant surge of admitted COVID-19 patients with severe ARDS. METHODS: This prospective cohort study conducted at a single regional university hospital between March 27 and April 15, 2020. Among 117 patients admitted to ICU, 67 patients (57.3%) presented with proven SARS-CoV-2 infection with severe ARDS requiring PP. After accelerated simulation training, 109 volunteers including surgeons, physicians, nurses and physiotherapists, multiple dedicated teams performed daily multiple PP following a systematic checklist. Patient demographics and PP data were collected. Patient safety and health care workers safety were assessed. RESULTS: Among 117 patients admitted to ICU, 67 patients (57.3%) required PP. Overall, 53 (79%) were male, with a median age of 68.5 years and median body mass index of 29.3 kg/m. A total of 384 PP were performed. Overall, complication occurred in 34 PP (8.8%) and led to PP cancelation in 4 patients (1%). Regarding health care workers safety, four health care workers presented with potential COVID-19 related symptoms and none was positive. CONCLUSIONS: To overcome the surge of critically ill COVID-19 patients, reallocating health care workers to targeted medical tasks beyond their respective expertise such as PP was safe.

Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization.

Background: The ongoing coronavirus disease 2019 pandemic has severely affected the United States. During infectious disease outbreaks, forecasting models are often developed to inform resource utilization. Pregnancy and delivery pose unique challenges, given the altered maternal immune system and the fact that most American women choose to deliver in the hospital setting. Objective: This study aimed to forecast the first pandemic wave of coronavirus disease 2019 in the general population and the incidence of severe, critical, and fatal coronavirus disease 2019 cases during delivery hospitalization in the United States. Study Design: We used a phenomenological model to forecast the incidence of the first wave of coronavirus disease 2019 in the United States. Incidence data from March 1, 2020, to April 14, 2020, were used to calibrate the generalized logistic growth model. Subsequently, Monte Carlo simulation was performed for each week from March 1, 2020, to estimate the incidence of coronavirus disease 2019 for delivery hospitalizations during the first pandemic wave using the available data estimate. Results: From March 1, 2020, our model forecasted a total of 860,475 cases of coronavirus disease 2019 in the general population across the United States for the first pandemic wave. The cumulative incidence of coronavirus disease 2019 during delivery hospitalization is anticipated to be 16,601 (95% confidence interval, 9711-23,491) cases, 3308 (95% confidence interval, 1755-4861) cases of which are expected to be severe, 681 (95% confidence interval, 1324-1038) critical, and 52 (95% confidence interval, 23-81) fatal. Assuming similar baseline maternal mortality rate as the year 2018, we projected an increase in maternal mortality rate in the United States to at least 18.7 (95% confidence interval, 18.0-19.5) deaths per 100,000 live births as a direct result of coronavirus disease 2019. Conclusion: Coronavirus disease 2019 in pregnant women is expected to severely affect obstetrical care. From March 1, 2020, we forecast 3308 severe and 681 critical cases with about 52 coronavirus disease 2019-related maternal mortalities during delivery hospitalization for the first pandemic wave in the United States. These results are significant for informing counseling and resource allocation.

Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.

The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease, resulting in the rapid dissemination of underdeveloped and potentially biased models, which may exacerbate the disparities gap. We believe there is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared COVID-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.

The principle of salvage in the context of COVID-19.

The prioritisation of scarce resources has a particular urgency within the context of the COVID-19 pandemic crisis. This paper sets out a hypothetical case of Patient X (who is a nurse) and Patient Y (who is a non-health care worker). They are both in need of a ventilator due to COVID-19 with the same clinical situation and expected outcomes. However, there is only one ventilator available. In addressing the question of who should get priority, the proposal is made that the answer may lie in how the pandemic is metaphorically described using military terms. If nursing is understood to take place at the 'frontline' in the 'battle' against COVID-19, a principle of military medical ethics-namely the principle of salvage-can offer guidance on how to prioritise access to a life-saving resource in such a situation. This principle of salvage purports a moral direction to return wounded soldiers back to duty on the battlefield. Applying this principle to the hypothetical case, this paper proposes that Patient X (who is a nurse) should get priority of access to the ventilator so that he/she can return to the 'frontline' in the fight against COVID-19.

Optimal allocation of limited test resources for the quantification of COVID-19 infections.

The systematic identification of infected individuals is critical for the containment of the COVID-19 pandemic. Currently, the spread of the disease is mostly quantified by the reported numbers of infections, hospitalisations, recoveries and deaths; these quantities inform epidemiology models that provide forecasts for the spread of the epidemic and guide policy making. The veracity of these forecasts depends on the discrepancy between the numbers of reported, and unreported yet infectious, individuals. We combine Bayesian experimental design with an epidemiology model and propose a methodology for the optimal allocation of limited testing resources in space and time, which maximises the information gain for such unreported infections. The proposed approach is applicable at the onset and spread of the epidemic and can forewarn of a possible recurrence of the disease after relaxation of interventions. We examine its application in Switzerland; the open source software is, however, readily adaptable to countries around the world. We find that following the proposed methodology can lead to vastly less uncertain predictions for the spread of the disease, thus improving estimates of the effective reproduction number and the future number of unreported infections. This information can provide timely and systematic guidance for the effective identification of infectious individuals and for decision-making regarding lockdown measures and the distribution of vaccines.

On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic.

OBJECTIVE: This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic. MATERIALS AND METHODS: The system presented is simulated with disease impact statistics from the Institute of Health Metrics, Centers for Disease Control and Prevention, and Census Bureau. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications. RESULTS: The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93% to 95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74 ± 30.8% in simulations with 5 states to 93.50 ± 0.003% with 50 states. CONCLUSIONS: These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.

A survey of US hospitals' criteria for the allocation of remdesivir to treat COVID-19.

PURPOSE: To determine how hospitals across the United States determined allocation criteria for remdesivir, approved in May 2020 for treatment of coronavirus disease 2019 (COVID-19) through an emergency use authorization, while maintaining fair and ethical distribution when patient needs exceeded supply. METHODS: A electronic survey inquiring as to how institutions determined remdesivir allocation was developed. On June 17, 2020, an invitation with a link to the survey was posted on the Vizient Pharmacy Network Community pages and via email to the American College of Clinical Pharmacy's Infectious Disease Practice and Research Network listserver. RESULTS: 66 institutions representing 28 states responded to the survey. The results showed that 98% of surveyed institutions used a multidisciplinary team to develop remdesivir allocation criteria. A majority of those teams included clinical pharmacists (indicated by 97% of respondents), adult infectious diseases physicians (94%), and/or adult intensivists (69%). Many teams included adult hospitalists (49.2%) and/or ethicists (35.4%). Of the surveyed institutions, 59% indicated that all patients with COVID-19 were evaluated for treatment, and 50% delegated initial patient identification for potential remdesivir use to treating physicians. Prioritization of remdesivir allocation was often determined on a "first come, first served" basis (47% of respondents), according to a patient's respiratory status (28.8%) and/or clinical course (24.2%), and/or by random lottery (22.7%). Laboratory parameters (10.6%), comorbidities (4.5%), and essential worker status (4.5%) were rarely included in allocation criteria; no respondents reported consideration of socioeconomic disadvantage or use of a validated scoring system. CONCLUSION: The COVID-19 pandemic has exposed the inconsistencies of US medical centers' methods for allocating a limited pharmacotherapy resource that required rapid, fair, ethical and equitable distribution. The medical community, with citizen participation, needs to develop systems to continuously reevaluate criteria for treatment allocation as additional guidance and data emerge.