Measuring and driving hospitalist value: Expanding beyond wRVUs

02/08/2024|Serpens Medical|

Introduction

Hospitalist productivity and compensation are multifaceted issues influenced by the evolving nature of clinical work. As hospitalists handle increasingly complex patients and navigate the intricacies of modern hospital systems, they often face unpredictable and heavy workloads. These clinical roles have expanded to include duties with high value but lower productivity, such as triage, which further broadens the scope of hospitalist responsibilities. At the same time, hospital financing models are changing, with hospitals facing rising direct costs. This shift may prompt hospitals to adopt high-productivity hospitalist models to offset costs. However, such approaches can lead to unintended consequences, including deteriorating clinician wellness, reduced patient care quality and safety, and misalignment with institutional goals.

Hospitalists are tasked with managing some of the most complex patients in healthcare and leading substantial institutional initiatives. During the COVID-19 pandemic, their roles became even more crucial as they adapted to the rapidly changing demands of health systems and led significant operational surge efforts. The increased strain on hospitalists has highlighted concerns such as stress, anxiety, overwork, and burnout, which were exacerbated during the pandemic but are not new phenomena.

With hospitalists playing a growing role in healthcare—now present in 75% of U.S. hospitals and numbering over 50,000—it is tempting to focus on the direct costs of their care models. However, research suggests that prioritizing productivity, with the expectation of seeing more patients, can increase waste and reduce care quality. While hospitalists may intuitively sense these trade-offs through their day-to-day work, rigorous research is needed to fully understand the unintended consequences of such models. Reimagining hospitalist clinical work can help optimize care models that support both hospitalists and institutions, ultimately reducing overall care costs.

This article explores three key ideas: (1) the current emphasis on productivity may harm hospitalists, patients, and institutional outcomes; (2) we need to examine both measurable outcomes and implicit system-level assumptions to better understand ideal workloads; and (3) clinical staffing strategies must evolve and be evidence-based to optimize outcomes. Recognizing that hospitalists contribute to various high-value efforts and scholarly activities, this discussion focuses primarily on clinical care models.

The Trade-offs of Productivity

Many hospitalist staffing models are financially driven, as most hospitalist groups depend on hospital subsidies to cover salaries, given that provider billing alone is insufficient. In response, hospitals may attempt to improve cost efficiency by increasing the number of patients hospitalists see within a given timeframe. However, preliminary studies indicate that higher workloads negatively impact hospitalists’ well-being, mental health, and job performance, while also leading to poorer hospital operational and financial outcomes.

Burnout in hospital medicine is frequently attributed to excessive clinical workloads, such as high patient-to-clinician ratios and unsustainable expectations. Research shows that burnout affects both the quality of patient care and the financial performance of healthcare institutions. Physicians experiencing burnout are more likely to leave their jobs, and those who stay may reduce the amount of time they dedicate to patient care. Increasing workloads have been linked to declines in hospitalist job performance, including perceived care quality and operational outcomes like patient length of stay.

In a study, 40% of physicians reported that their inpatient workloads exceeded safe levels at least once a month, while hospital resources often fail to keep up with these increasing demands. Another study by Elliott et al. found that when physicians saw more than 15 patients per shift, patient length of stay and hospital charges rose significantly. Kamalahmadi et al. highlighted that major academic medical centers (AMCs) could improve hospital-wide flow by focusing on reducing hospitalist workloads, thereby shortening patient stays. Community hospitals also benefited from workload reductions, though the underlying reasons differed due to their unique patient populations.

Moreover, quality improvement efforts can suffer under heavy workloads. Kara et al. reported that clinicians felt that geographically cohorted patients—grouped together for operational efficiency—negatively impacted patient safety, collaboration, and morale when patient loads were high. These findings suggest that traditional measures of efficiency fail to capture important factors that have financial and clinical consequences. More holistic assessments could save healthcare systems money while safeguarding hospitalists’ well-being.

Aligning Clinician-, Patient-, and Institutional-Centric Measures

To address these issues, we propose a framework based on the job demands-resources model of burnout and an integrated approach to worker health and safety. This framework connects hospitalist workloads to outcomes for both clinicians and patients, as well as job performance. Research indicates a dose-response relationship between increasing task load and burnout, emphasizing the importance of balancing job demands with available resources to maintain efficiency and high-quality care.

Our model underscores that job demands and resources also influence institutional financial outcomes, highlighting the need to rethink how productivity is measured. Current metrics are inadequate, as they often overlook the broader impacts on clinicians and the healthcare system. We propose incorporating more holistic measures that reflect institutional priorities while leveraging electronic health records (EHRs) and human-centered care indicators. These measures would more accurately reflect the value hospitalists bring to patients and the broader health system (as shown in Table 1).

FI GURE 1  Conceptual model for hospitalist clinical work.

TA BL E 1 Potential measures for hospitalist clinical work

MeasureProsConsCurrent or future use examples
ProductivitywRVU/cFTESimple and data typically available.May not appropriately account for trueTypical measurements for productivity and

measures

Encounters/cFTE

effort (i.e., effects of moonlighting, how cFTE is defined, how leave is accounted for, non‐RVU generating work). Need to account for the impact of staffing models (e.g., APP, learners).

for benchmarking.

Workload wRVU/cFTE Measure quantity of work and in some cases complexity

While some measures are readily available, task load would require surveying or

Task load is often used in high‐risk industries (NASA), but is not utilized in

Encounters/cFTE

Task load

Patient complexity (case mix index, Charlson comorbidity)

EHR measures (total EHR time, work outside of work, time on documentation, inbox time, distractions)

of work.

Accessible from the EHR; the potential for real‐time measures.

developing some measures from EHR. Need to account for staffing models (APP, learners). Need to account for patient complexity and type of work. Other modifiers may need to be accounted for such as geography and patient population/required tasks and non‐RVU generating work.

Hospitalists work variable hours (defining after‐hour work may be challenging). May not be efficient to extract large volumes of data. Does not capture

all work.

daily operations in medicine. Could be utilized to periodically gauge the perception of workload in conjunction with more typical measures of workload. Patient complexity should be considered when determining the appropriate workload. No standards currently exist.

There may be institutional reports on clinician‐specific work in the EHR. These measures could be incorporated into decision support tools.

Patient safety culture

Team culture and safety surveys Gives an understanding of how

work environment and staffing models support patient care and communication.

Requires surveying. May be administered annually and paired with global questions about workload, but typically not paired with discrete measures of workload (e.g., what quantity of work leads to poor outcomes on the surveys).

(Continued)

In high‐risk industries, both within and outside of medicine, attention and cognitive load are increasingly areas of focus as research shows fractured attention leads to increased processing time on complex tasks, impaired working memory, and bias.17 A better understanding of these clinician‐centric measures and how they intersect with staffing models, patient outcomes, and institu- tional goals would help us design work environments and staffing models that are more humane while assessing the value to which our current measures of productivity are insensitive. Importantly, track- ing these measures in real‐time and eventually integrating predictive analytics will be key to understanding how changing workloads may impact vital outcomes and could offer the opportunity to proactively course‐correct as models evolve.

DEVELOP STAFFING STRATEGIES THAT ARE DATA‐ AND EVIDENCE‐ BASED

Integrated information systems must be coupled with workflows and business process management, which would allow for an iterative improvement process informed by data18 and aligned with hospital priorities.19 These data may come from the EHR and from real‐time assessments of clinician workload, task load, and biosensor technol- ogy to understand the true impact of work on the workforce and how staffing models affect a hospitalist’s ability to do their job. Easy‐to‐ use workforce assessments must be implemented in addition to or in place of the once‐a‐year wellness surveys and paired with opera- tional decision support tools. We must also understand operational inefficiencies and recognize that staffing models and needs will evolve over time and these information systems and decision tools must evolve with them.

Ensuring the return‐on‐investment will be a central focus for any hospital. Kamalahmadi et al. 6 showed that reducing hospitalists’ census could lead to ~$1.5 million in annual savings— solely by implementing optimal staffing strategies that include lowering hospitalist census (although models vary based upon whether the institution is academic or community and patient complexity). Pre- sumably, in addition to cost savings, busy AMCs that run at capacity could then focus on bringing in more profit‐generating admissions such as surgeries. Lastly, the cost of burnout and attrition is high. While on face value, some may believe that turnover is cost‐effective (e.g., more junior hospitalists may have lower salaries), the literature would suggest that much of that savings may be negated by additional recruitment costs, costs from burnout‐driven full‐time equivalent reduction, and worse outcomes including increased mortality20 from an inexperienced workforce.

LOOKING FORWARD 

We must realign the outcomes to which hospitalists and healthcare systems subscribe. As a starting point, all measures of workload should have balancing measures. Understanding what constitutes

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total work and developing robust mechanisms to seamlessly measure this global measure must be undertaken. The field of hospital medicine has an incredible opportunity before it. With nearly unlimited access to data, agile staffing models, and diverse models of care implemented across the United States, hospitalists can again serve as the forerunners of American healthcare reform. As part of these efforts, we have a responsibility to develop measurements of total work, understand the effects on clinicians, patients, and institutional outcomes, and begin to aim toward thriving.

CONFLICT OF  INTEREST

The authors declare no conflict of interest.

ORCID 

Marisha Burden http://orcid.org/0000-0002-8262-3994

TWITTER 

Marisha Burden @marishaburden

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