Journal of Healthcare Management最新文献

Goal: This study was developed to explicate underlying organizational factors contributing to the deterioration of primary care clinicians' mental health during the COVID-19 pandemic.

Methods: Using data from the Larry A. Green Center for the Advancement of Primary Health Care for the Public Good's national survey of primary care clinicians from March 2020 to March 2022, a multidisciplinary team analyzed more than 11,150 open-ended comments. Phase 1 of the analysis happened in real-time as surveys were returned, using deductive and inductive coding. Phase 2 used grounded theory to identify emergent themes. Qualitative findings were triangulated with the survey's quantitative data.

Principal findings: The clinicians shifted from feelings of anxiety and uncertainty at the start of the pandemic to isolation, lack of fulfillment, moral injury, and plans to leave the profession. The frequency with which they spoke of depression, burnout, and moral injury was striking. The contributors to this distress included crushing workloads, worsening staff shortages, and insufficient reimbursement. Consequences, both felt and anticipated, included fatigue and demoralization from the inability to manage escalating workloads. Survey findings identified responses that could alleviate the mental health crisis, namely: (1) measuring and customizing workloads based on work capacity; (2) quantifying resources needed to return to sufficient staffing levels; (3) promoting state and federal support for sustainable practice infrastructures with less administrative burden; and (4) creating patient visits of different lengths to rebuild relationships and trust and facilitate more accurate diagnoses.

Practical applications: Attention to clinicians' mental health should be rapidly directed to on-demand, confidential mental health support so they can receive the care they need and not worry about any stigma or loss of license for accepting that help. Interventions that address work-life balance, workload, and resources can improve care, support retention of the critically important primary care workforce, and attract more trainees to primary care careers.

Goal: Boarding emergency department (ED) patients is associated with reductions in quality of care, patient safety and experience, and ED operational efficiency. However, ED boarding is ultimately reflective of inefficiencies in hospital capacity management. The ability of a hospital to accommodate variability in patient flow presumably affects its financial performance, but this relationship is not well studied. We investigated the relationship between ED boarding and hospital financial performance measures. Our objective was to see if there was an association between key financial measures of business performance and limitations in patient progression efficiency, as evidenced by ED boarding.

Methods: Cross-sectional ED operational data were collected from the Emergency Department Benchmarking Alliance, a voluntarily self-reporting operational database that includes 54% of EDs in the United States. Freestanding EDs, pediatric EDs and EDs with missing boarding data were excluded. The key operational outcome variable was boarding time. We reviewed the financial information of these nonprofit institutions by accessing their Internal Revenue Service Form 990. We examined standard measures of financial performance, including return on equity, total margin, total asset turnover, and equity multiplier (EM). We studied these associations using quantile regressions of added ED volume, ED admission percentage, urban versus nonurban ED site location, trauma status, and percentage of the population receiving Medicare and Medicaid as covariates in the regression models.

Principal findings: Operational data were available for 892 EDs from 31 states. Of those, 127 reported a Form 990 in the year corresponding to the ED boarding measures. Median boarding time across EDs was 148 min (interquartile range [IQR]: 100-216). A significant relationship exists between boarding and the EM, along with a negative association with the hospital's total profit margin in the highest-performing hospitals (by profit margin percentage). After adjusting for the covariates in the regression model, we found that for every 10 min above 90 min of boarding, the mean EM for the top quartile increased from 245.8% to 249.5% (p < .001). In hospitals in the top 90th percentile of total margin, every 10 min beyond the median ED boarding interval led to a decrease in total margin of 0.24%.

Practical applications: Using the largest available national registry of ED operational data and concordant nonprofit financial reports, higher boarding among the highest-profitability hospitals (i.e., top 10%) is associated with a drag on profit margin, while hospitals with the highest boarding are associated with the highest leverage (i.e., indicated by the EM). These relationships suggest an association between a key ED indicator of hospital capacity management and overall institutional financial performance.

Goal: A lack of improvement in productivity in recent years may be the result of suboptimal measurement of productivity. Hospitals and clinics benefit from external benchmarks that allow assessment of clinical productivity. Work relative value units have long served as a common currency for this purpose. Productivity is determined by comparing work relative value units to full-time equivalents (FTEs), but FTEs do not have a universal or standardized definition, which could cause problems. We propose a new clinical labor input measure-"clinic time"-as a substitute for using the reported measure of FTEs.

Methods: In this observational validation study, we used data from a cluster randomized trial to compare FTE with clinic time. We compared these two productivity measures graphically. For validation, we estimated two separate ordinary least squares (OLS) regression models. To validate and simultaneously adjust for endogeneity, we used instrumental variables (IV) regression with the proportion of days in a pay period that were federal holidays as an instrument. We used productivity data collected between 2018 and 2020 from Veterans Health Administration (VA) cardiology and orthopedics providers as part of a 2-year cluster randomized trial of medical scribes mandated by the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act of 2018.

Principal findings: Our cohort included 654 unique providers. For both productivity variables, the values for patients per clinic day were consistently higher than those for patients per day per FTE. To validate these measures, we estimated separate OLS and IV regression models, predicting wait times from the two productivity measures. The slopes from the two productivity measures were positive and small in magnitude with OLS, but negative and large in magnitude with IV regression. The magnitude of the slope for patients per clinic day was much larger than the slope for patients per day per FTE. Current metrics that rely on FTE data may suffer from self-report bias and low reporting frequency. Using clinic time as an alternative is an effective way to mitigate these biases.

Practical applications: Measuring productivity accurately is essential because provider productivity plays an important role in facilitating clinic operations outcomes. Most importantly, tracking a more valid productivity metric is a concrete, cost-effective management tactic to improve the provision of care in the long term.

Goal: Growing numbers of hospitals and payers are using call centers to answer patients' clinical and administrative questions, schedule appointments, address billing issues, and offer supplementary care during public health emergencies and national disasters. In 2020, the Veterans Health Administration (VA) implemented VA Health Connect, an enterprise-wide initiative to modernize call centers. VA Health Connect is designed to improve the care experience with the convenience, flexibility, and simplicity of a single toll-free number connected to a range of 24/7 virtual services. The services are organized into four areas: administrative guidance for scheduling and general inquiries; pharmacy support for medication matters; clinical triage for evaluation of symptoms and recommended care; and virtual visits with providers for urgent and episodic care. Through a qualitative evaluation of VA Health Connect, we sought to identify the factors that affected the development of this program and to compile considerations to support the implementation of other enterprise-wide initiatives.

Methods: The evaluation team interviewed 29 clinical and administrative leads from across the VA. These leads were responsible for the modernization of their local service networks. PhD-level qualitative methodologists conducted the interviews, asking participants to reflect on barriers and facilitators to modernization and implementation. The team employed a rapid qualitative analytic approach commonly used in healthcare research to distill robust results.

Principal findings: A review of the early implementation of VA Health Connect found: (1) deadlines proved challenging but provided momentum for the initiative; (2) a balance between standardized processes and local adaptations facilitated implementation; (3) attention to staffing, hiring, and training of call center staff before implementation expedited workflows; (4) establishing national and local leadership commitment to the innovation from the onset increased team cohesion and efficacy; and (5) anticipating information technology infrastructure needs prevented delays to modernization and implementation.

Practical applications: Our findings suggest that healthcare systems would benefit from anticipating likely obstacles (e.g., delays in software implementations and negotiations with unions), thus providing ample time to secure leadership buy-in and identify local champions, communicating early and often, and supporting flexible implementation to meet local needs. VA leadership can use this evaluation to refine implementation, and it could also have important implications for regulators, federal health exchanges, insurers, and other healthcare systems when determining resource levels for call centers.