Matching medical staff to long term care facilities to respond to COVID-19 outbreak.

BACKGROUND: Staff shortage is a long-standing issue in long term care facilities (LTCFs) that worsened with the COVID-19 outbreak. Different states in the US have employed various tools to alleviate this issue in LTCFs. We describe the actions taken by the Commonwealth of Massachusetts to assist LTCFs in addressing the staff shortage issue and their outcomes. Therefore, the main question of this study is how to create a central mechanism to allocate severely limited medical staff to healthcare centers during emergencies. METHODS: For the Commonwealth of Massachusetts, we developed a mathematical programming model to match severely limited available staff with LTCF demand requests submitted through a designed portal. To find feasible matches and prioritize facility needs, we incorporated restrictions and preferences for both sides. For staff, we considered maximum mileage they are willing to travel, available by date, and short- or long-term work preferences. For LTCFs, we considered their demand quantities for different positions and the level of urgency for their demand. As a secondary goal of this study, by using the feedback entries data received from the LTCFs on their matches, we developed statistical models to determine the most salient features that induced the LTCFs to submit feedback. RESULTS: We used the developed portal to complete about 150 matching sessions in 14 months to match staff to LTCFs in Massachusetts. LTCFs provided feedback for 2,542 matches including 2,064 intentions to hire the matched staff during this time. Further analysis indicated that nursing homes and facilities that entered higher levels of demand to the portal were more likely to provide feedback on the matches and facilities that were prioritized in the matching process due to whole facility testing or low staffing levels were less likely to do so. On the staffing side, matches that involved more experienced staff and staff who can work afternoons, evenings, and overnight were more likely to generate feedback from the facility that they were matched to. CONCLUSION: Developing a central matching framework to match medical staff to LTCFs at the time of a public health emergency could be an efficient tool for responding to staffing shortages. Such central approaches that help allocate a severely limited resource efficiently during a public emergency can be developed and used for different resource types, as well as provide crucial demand and supply information in different regions and/or demographics.

Longitudinal Associations of Staff Shortages and Staff Levels with Health Outcomes in Nursing Homes.

OBJECTIVES: To examine whether facility-reported staff shortages and total staff levels were independently associated with changes in nursing home (NH) outcomes in 2020. DESIGN: Longitudinal cohort study. SETTING AND PARTICIPANTS: A total of 8466 NHs with staffing and outcome data. METHODS: This study used NH COVID-19 Public File (2020), Nursing Home Compare (2019-2020), and Payroll-Based Journal data (2019-2020). Outcome measures included the percentage of long-stay residents in a facility with declines in activities in daily living (ADLs), decreases in mobility, weight loss, and pressure ulcers in 2020 Q2, 2020 Q3, and 2020 Q4. Independent variables were whether NHs reported any shortage of aides or licensed nurses and total staff hours per resident day (HPRD). Separate 2-level (NH, state) Hierarchical Generalized Linear Mixed models examined the association of facility-reported shortages and staff hours with key NH resident outcomes, controlling for NH characteristics and COVID-19 infections. RESULTS: The weekly percentage of NHs reporting any staff shortage averaged 20%. Total staff HPRD increased slightly from 3.7 in 2019 to 3.8 in 2020. Health outcomes were stable during 2019 and 2020 Q1 but worsened substantially starting in 2020 Q2. For example, the percentage of residents with mobility loss increased from 16.2% in 2020 Q1 to 27.9% in 2020 Q4. Facility-reported staff shortages were associated with an increase in the proportion of residents with an ADL decline (0.54 percentage points), mobility loss (0.80 percentage points), weight loss (0.22 percentage points), and pressure ulcers (0.22 percentage points) (all P < .01). Total staff HPRD was not associated with changes in any outcomes (all P > .05). CONCLUSIONS AND IMPLICATIONS: NHs reported worsened health outcomes among long-stay residents in 2020, with worse outcomes found among facilities that reported staff shortages but not among those with lower total staff levels. Facility-reported shortages provide important quality information during the COVID-19 pandemic.

Staffing Shortages, Staffing Hours, and Resident Deaths in US Nursing Homes During the COVID-19 Pandemic.

OBJECTIVES: Staffing shortages at nursing homes during the COVID-19 pandemic may have impacted care providers' staffing hours and affected residents' care and outcomes. This study examines the association of staffing shortages with staffing hours and resident deaths in nursing homes during the COVID-19 pandemic. DESIGN: This study measured staffing hours per resident using payroll data and measured weekly resident deaths and staffing shortages using the Centers for Disease Control and Prevention's National Healthcare Safety Network data. Multivariate linear regressions with facility and county-week fixed effects were used to investigate the association of staffing shortages with staffing hours and resident deaths. SETTING AND PARTICIPANTS: 15,212 nursing homes. MEASURES: The primary outcomes included staffing hours per resident of registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs) and weekly total deaths per 100 residents. RESULTS: Between May 31, 2020, and May 15, 2022, 18.4% to 33.3% of nursing homes reported staffing shortages during any week. Staffing shortages were associated with lower staffing hours per resident with a 0.009 decrease in RN hours per resident (95% CI 0.005-0.014), a 0.014 decrease in LPN hours per resident (95% CI 0.010-0.018), and a 0.050 decrease in CNA hours per resident (95% CI 0.043-0.057). These are equivalent to a 1.8%, 1.7%, and 2.4% decline, respectively. There was a positive association between staffing shortages and resident deaths with 0.068 (95% CI 0.048-0.088) total deaths per 100 residents. This was equivalent to an increase of 10.5%. CONCLUSION AND IMPLICATIONS: Our results showed that self-reported staffing shortages were associated with a statistically significant decrease in staffing hours and with a statistically significant increase in resident deaths. These results suggest that addressing staffing shortages in nursing homes can save lives.

Impact of using a centralized matching process on nursing home staffing.

OBJECTIVE: To examine the effectiveness of adopting a novel centralized matching process for reducing staff shortages in Massachusetts nursing homes during the COVID-19 pandemic. METHODS: This study involved several datasets and 216 Massachusetts nursing homes that used a novel online portal to enter demand for nursing staff from May 2020 to April 2021. RESULTS: There were significant associations between the staff-to-resident ratio and demand entries lagged by three and four weeks, and no significant associations between the staff-to-resident ratio and demand entries lagged by one and two weeks. In contrast, we found significant associations between the staff-to-resident ratio and the number of generated staff matches lagged by one, two and three weeks, with larger impacts overall. CONCLUSION: This study shows how adopting a centralized matching process may expedite and increase improvement in the staff-to-resident ratio in nursing homes, compared with the setup in which nursing homes need to seek nurses on their own.

Medical Staff Shortages and the Performance of Outpatient Clinics in Poland during the COVID-19 Pandemic.

Unlike many industries, healthcare was simultaneously affected by the COVID-19 pandemic in two opposite ways. On the one hand, the industry faced shortages and overload of many medical representatives such as nurses and infectious disease professionals, but on the other, many medical professionals such as dentists were left with considerably reduced demand. The objective of this paper is to study the efficiency of medical staff allocation and the performance of small and medium sized outpatient clinics in Poland during the COVID-19 pandemic. Owing to the contemporaneity of this problem, we have employed a multiple case study approach. Our sample comprises 5 small and medium-sized outpatient clinics located in Poland in the 3-year period 2019-2021. The results indicate a considerable percentage of medical staff employed in small and medium-sized outpatient clinics remained outside the pandemic, despite their potential provision of healthcare services. Four of the five clinics studied remained passive towards the pandemic. In view of future pandemics, the indications we provide have practical implications for outpatient clinics executives and public health policymakers.

Optimal nursing home service scheduling under Covid-19 related probabilistic staff shortage: A two-stage stochastic programming approach

Maintaining an appropriate staffing level is essential to providing a healthy workplace environment at nursing homes and ensuring quality care among residents. With the widespread Covid-19 pandemic, staff absenteeism frequently occurs due to mandatory quarantine and providing care to their inflicted family members. Even though some of the staff show up for work, they may have to perform additional pandemic-related protection duties. In combination, these changes lead to an uncertain reduction in the quantity of care each staff member able to provide in a future shift. To alleviate the staff shortage concern and maintain the necessary care quantity, we study the optimal shift scheduling problem for a skilled nursing facility under probabilistic staff shortage in the presence of pandemic-related service provision disruptions. We apply a two-stage stochastic programming approach to our study. Our objective is to assign staff (i.e., certified nursing aids) to shifts to minimize the total staffing cost associated with contract staff workload, the adjusted workload for the changing resident demand, and extra workload due to required sanitization. Thus, the uncertainties considered arise from probabilistic staff shortage in addition to resident service need fluctuation. We model the former source of uncertainty with a geometric random variable for each staffer. In a proof-of-the-concept study, we consider realistic COVID-19 pandemic response measures recommended by the Indiana state government. We extract payment parameter estimates from the COVID-19 Nursing Home Dataset publicly available by the Centers for Medicare and Medicaid Services (CMS). We conclude with our numerical experiments that when a skilled nursing facility is at low risk of the pandemic, the absenteeism rate and staff workload increase slightly, thus maintaining the current staffing level can still handle the service disruptions. On the other hand, under high-risk circumstances, with the sharp increase of the absence rate and workload, a care facility likely needs to hire additional full-time staff as soon as possible. Our research offers insights into staff shift scheduling in the face of uncertain staff shortages and service disruption due to pandemics and prolonged disasters. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

[One year corona pandemic management in a 300-bed hospital : Report of the pandemic officer]. / Ein Jahr Coronapandemiemanagement an einem 300-Betten-Krankenhaus : Report der Pandemiebeauftragten.

BACKGROUND: From the point of view of the pandemic officer of a 300-bed specialty care hospital, the first year of the SARS-CoV­2 (Severe Acute Respiratory Syndrome Coronavirus Type 2) pandemic with all the challenges for daily clinical routine was reviewed on the basis of the data collected with the aim of making future pandemic management easier to plan. METHODS: The Caritas Hospital St. Josef in Regensburg employs around 1330 employees and treats almost 20,000 inpatients annually. All records of the pandemic officer between 1 March 2020 and 28 February 2021 were included in the present descriptive study. RESULTS: In all, 280 SARS-CoV-2-positive patients (mean age 68 ± 19 years) were treated during the observation period; 81% (n = 227) of these were treated as inpatients between November 2020 and February 2021. In addition to the analysis of these patients, an analysis of the test concept was carried out, both for the patients (53% of the tests in the so-called 2nd wave) and for the employees; 1633 PCR tests were carried out, with 5.6% of all employees being infected with SARS-CoV­2 in the course of the pandemic. In addition, other measures such as reducing the surgical program and other general measures were evaluated. CONCLUSION: Many established processes had to be changed in the hospital during the pandemic. Both staff shortages due to SARS-CoV-2-positive employees and the quarantine measures of contact persons led to further tension in the already tight staffing levels, especially in the nursing care sector, and made patient care more difficult. The test concept in the hospital was very complex and asymptomatic tests in particular showed a low hit rate, while being associated with high personnel costs.

Shortages of Staff in Nursing Homes During the COVID-19 Pandemic: What are the Driving Factors?

OBJECTIVES: During the Coronavirus Disease 2019 (COVID-19) pandemic, US nursing homes (NHs) have been under pressure to maintain staff levels with limited access to personal protection equipment (PPE). This study examines the prevalence and factors associated with shortages of NH staff during the COVID-19 pandemic. DESIGN: We obtained self-reported information on staff shortages, resident and staff exposure to COVID-19, and PPE availability from a survey conducted by the Centers for Medicare and Medicaid Services in May 2020. Multivariate logistic regressions of staff shortages with state fixed-effects were conducted to examine the effect of COVID-19 factors in NHs. SETTING AND PARTICIPANTS: 11,920 free-standing NHs. MEASURES: The dependent variables were self-reported shortages of licensed nurse staff, nurse aides, clinical staff, and other ancillary staff. We controlled for NH characteristics from the most recent Nursing Home Compare and Certification and Survey Provider Enhanced Reporting, market characteristics from Area Health Resources File, and state Medicaid reimbursement calculated from Truven data. RESULTS: Of the 11,920 NHs, 15.9%, 18.4%, 2.5%, and 9.8% reported shortages of licensed nurse staff, nurse aides, clinical staff, and other staff, respectively. Georgia and Minnesota reported the highest rates of shortages in licensed nurse and nurse aides (both >25%). Multivariate regressions suggest that shortages in licensed nurses and nurse aides were more likely in NHs having any resident with COVID-19 (adjusted odds ratio [AOR] = 1.44, 1.60, respectively) and any staff with COVID-19 (AOR = 1.37, 1.34, respectively). Having 1-week supply of PPE was associated with lower probability of staff shortages. NHs with a higher proportion of Medicare residents were less likely to experience shortages. CONCLUSIONS/IMPLICATIONS: Abundant staff shortages were reported by NHs and were mainly driven by COVID-19 factors. In the absence of appropriate staff, NHs may be unable to fulfill the requirement of infection control even under the risk of increased monetary penalties.