Journal of Healthcare Management最新文献

Goal: Value-based care is not simply a matter of cost, but also one of outcomes and harms per dollar spent. This definition encompasses three key components: healthcare delivery that is organized around patients' medical conditions, costs and outcomes that are actively and consistently measured, and information technology that enables the other two components. Our objective in this project was to implement and measure a systemwide high-value, evidence-based care initiative with five pillars of high-value practices.

Methods: We performed a quasi-experimental study from September 1, 2019, to August 31, 2022, of a new care program at the University of Texas Medical Branch. Drawing from the ABIM Foundation's Choosing Wisely Campaign, the program was based on five pillars-blood management and antimicrobial, laboratory, imaging, and opioid stewardship-with interdisciplinary teams led by institutional subject matter experts (i.e., administrative leaders) accompanied by nursing, information technology, pharmacy, and clinical and nonclinical personnel including faculty and trainees. Each pillar addressed two goals with targeted interventions to assess improvements during the first three fiscal years (FYs) of implementation. The targets were set at 10% improvement by the end of each FY. Monthly measurements were recorded for each FY.

Principal findings: We tracked performance toward 30 pillar goals and determined that the teams were successful in 50%, 50%, and 70% of their goals for FY 2020, 2021, and 2022, respectively. For example, in the antimicrobial stewardship FY 2021 pillar, one goal was to decrease meropenem days of therapy (DOT) by 10% (baseline was 45 DOT/1,000 patient days; the target was 40.5 DOT/1,000 patient days). We measured quarterly DOT/1,000 patient day rates of 32.02, 30.57, and 26.9, respectively, for a cumulative rate of 26.9. Critical interventions included engaging and empowering providers and service lines (including outliers whose performance was outside norms), educational conferences, and transparent data analyses.

Practical applications: We showed that a multidisciplinary approach to the implementation of an evidence-based, high-value care program through a partnership of engaged administrative leaders, providers, and trainees can result in sustainable and measurable high-value healthcare delivery. Specifically, structuring the program with pillars to address defined metrics resulted in progressive improvement in meeting value-based goals at the University of Texas Medical Branch. Also, challenges can be embraced as learning opportunities to inform value-based interventions that range from technological to educational tactics. The results at the University of Texas Medical Branch provide a benchmark for the implementation of a program that engages, empowers, and aligns innovative value-based care initiatives.

Goal: The COVID-19 pandemic, healthcare market disruptors, and new digital healthcare technologies have made a substantial impact on the delivery of healthcare services, highlighting the critical roles of leaders in hospitals and health systems. This study sought to understand the evolving roles of CEOs, CIOs, and other executive leaders in the postpandemic era and highlight the adaptability and strategic vision of executives in shaping the future of healthcare delivery.

Methods: Between October 2022 and May 2023, 51 interviews were conducted with CEOs, CIOs, and other executives responsible for delivering technology solutions for 33 nonprofit health systems in the United States. They were asked to describe their backgrounds; how information solutions and technologies were viewed within their organizations' strategy, operations, and governance; and the key characteristics of executive leaders.

Principal findings: The study has found that effective CEOs have an authentic belief in technology's role in achieving their organization's mission and that contemporary CIOs are strategic executive partners who align strategy with culture to improve care. This study examines how healthcare systems are creating digitally savvy executive leadership teams that operate in a new, integrated model that unites previously siloed functions.

Practical applications: Some healthcare CIOs are unprepared for current and future business challenges, and some CEOs are unsure how to leverage digital technologies and C-suite expertise to transform their organizations. This research provides insights into how the nation's health systems are building and sustaining leadership teams capable of adapting to the healthcare environment and accelerating organizational transformation.

Summary: Physician burnout, a significant problem in modern healthcare, adversely affects healthcare professionals and their organizations. This essay explores the potential of artificial intelligence (AI) to positively address this issue through its integration into the electronic health record and the automation of administrative tasks. Recent initiatives and research highlight the positive impact of AI assistants in alleviating physician burnout and suggest solutions to enhance physician well-being. By examining the causes and consequences of burnout, the promise of AI in healthcare, and its integration into electronic health record systems, this essay explores how AI can not only reduce physician burnout but also improve the efficiency of healthcare organizations. A roadmap provides a visualization of how AI could be integrated into electronic health records during the previsit, visit, and postvisit stages of a clinical encounter.

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.