The Productivity of Professions: Evidence from the Emergency Department

This paper studies the productivity of nurse practitioners (NPs) versus physicians performing overlapping tasks in Veterans Health Administration (VHA) emergency departments (EDs), exploiting a quasi-experiment created by the VHA’s December 2016 grant of full practice authority to NPs. The identification strategy instruments patient assignment to NPs versus physicians using quasi-random variation in the number of NPs on duty on a given ED-day, conditional on ED-by-time-category fixed effects. The sample covers 1.1 million ED visits across 44 VHA EDs from January 2017 to January 2020, seen by 1,348 physicians and 156 NPs. The instrument is validated by demonstrating balance in patient observable characteristics across values of the instrument, stability of IV estimates across 256 combinations of patient covariate controls, and absence of spillover effects from NP presence onto physician performance.

On average in the ED setting, NPs increase patient length of stay by 11 percent (approximately 18 additional minutes) and raise the cost of the ED visit by 7 percent (approximately $66 per visit). NPs raise the 30-day preventable hospitalization rate by 0.25 percentage points, a 20 percent increase relative to the mean. No statistically significant effect on 30-day mortality is detected (95 percent confidence interval: -0.34 to 0.11 percentage points). OLS estimates carry the opposite sign because NPs are assigned healthier patients in observational data; the IV design corrects for this selection.

The average NP-physician performance gap varies systematically by case complexity and severity. For the highest-complexity quartile of cases (by Elixhauser comorbidities), NPs increase ED costs by 12 percent and length of stay by 28 percent. For cases at or above the 95th percentile of severity (based on 30-day mortality by diagnosis), NPs increase ED costs by 25 percent, length of stay by 99 percent, and admissions by 26 percentage points (42 percent relative to the mean), while reducing 30-day preventable hospitalization by 3 percentage points — suggesting that NPs’ higher care intensity partially offsets worse intrinsic skill for the most severe cases. For lower-complexity cases, the cost and length-of-stay gaps are smaller, but NPs still significantly raise preventable hospitalizations.

NPs exhibit clinical decision-making patterns consistent with lower diagnostic skill: they are more likely to order consults (2.6 percentage points, or 11 percent of the mean), CT scans (1.2 percentage points, or 8.3 percent), and X-rays (2.0 percentage points, or 6.9 percent). NPs lower opioid prescriptions by 1.8 percentage points (20 percent of the mean) and raise antibiotic prescriptions by 4.0 percentage points (6.3 percent of the mean), consistent with threshold adjustment under lower diagnostic skill with asymmetric error costs. Downstream, patients treated by NPs incur similar opioid use disorder rates despite lower opioid prescribing, and higher infection-related return visit rates despite higher antibiotic prescribing.

Counterfactual analysis finds that allocating one quarter of ED patients to NPs increases net spending by $129 million per year to the VHA after accounting for NPs’ lower wages (approximately half of physicians’). However, deploying NPs exclusively to the least-complex quarter of cases reduces net spending to approximately one-fifth of this amount.

A distributional analysis deconvolving provider-specific IV estimates reveals that within-profession productivity variation substantially exceeds the average between-profession gap. The interquartile range in annual spending attributable to provider productivity within each profession is approximately $900,000, roughly three times the mean annual spending difference between the average NP and the average physician. A randomly chosen NP outperforms a randomly chosen physician in up to 38 percent of pairs. Within professions, individual provider productivity shows essentially no relationship with wages or case complexity assigned, whereas between professions, case assignment and wages are strongly sorted by professional class.

Q: What is the core research question? A: The paper asks whether NPs and physicians, who perform overlapping tasks in the ED but differ sharply in training, selectivity, and pay, differ in productivity, and how that average between-profession difference compares to productivity variation within each profession. It also asks what mechanisms drive any observed gap and how case assignment responds to provider skill differences.

Q: What is the identification strategy and why is it credible? A: The authors instrument patient assignment to NPs with the number of NPs on duty on the ED-day, conditional on ED-by-year, ED-by-month, ED-by-day-of-week, and ED-by-hour fixed effects. Credibility rests on: provider schedules being set months in advance, decoupling NP availability from arriving patient characteristics; patient characteristics being well balanced across values of the instrument conditional on fixed effects; IV estimates being stable across all 256 covariate-control combinations; and on-duty physician and NP characteristics also being balanced across the instrument.

Q: What are the main average effects of NPs on resource use? A: IV estimates show NPs increase patient length of stay by 11 percent (approximately 18 minutes) and ED cost by 7 percent (approximately $66 per visit). There is no significant average effect on inpatient admissions in the overall sample, though NPs significantly raise admissions for high-severity cases.

Q: What is the effect of NPs on patient health outcomes? A: NPs raise 30-day preventable hospitalizations by 0.25 percentage points, a 20 percent increase relative to the mean. The 95 percent confidence interval for 30-day mortality is -0.34 to 0.11 percentage points, implying no statistically significant mortality effect in the overall sample.

Q: Why do OLS and IV estimates have opposite signs? A: In observational data, NPs treat healthier patients than physicians: NP patients are younger (60.7 versus 62.5 years), have fewer Elixhauser comorbidities (3.2 versus 3.7), and have fewer prior inpatient stays (0.4 versus 0.7). This selection causes OLS estimates of NP effects to be negative. The IV corrects for this by exploiting quasi-random variation in NP availability; IV estimates are stable across all combinations of patient controls, consistent with the instrument being orthogonal to unobservable patient health.

Q: How does the NP-physician performance gap vary with case complexity and severity? A: For the highest-complexity quartile, NPs increase length of stay by 28 percent and ED costs by 12 percent without a significant preventable hospitalization effect. For cases at or above the 95th severity percentile, NPs increase length of stay by 99 percent, ED costs by 25 percent, and admissions by 26 percentage points (42 percent relative to the mean), while reducing 30-day preventable hospitalization by 3 percentage points. For lower-complexity quartiles, NPs show smaller cost and length-of-stay effects but significantly raise preventable hospitalizations, suggesting the higher care intensity at high severity compensates for lower skill.

Q: What does the heterogeneity by severity imply for optimal case assignment? A: The pattern is consistent with skill-task matching: NPs have a comparative and absolute disadvantage in complex cases, so optimal assignment directs less complex cases to NPs and fewer patients to NPs when physicians are more available. Empirically, NPs are indeed assigned healthier patients from the available pool, and are assigned a modestly smaller share when the ED is less busy.

Q: What mechanisms explain the average NP-physician gap? A: Three mechanisms are examined. First, experience: a one-standard-deviation increase in specific experience is associated with a 5.8 percent decline in the NP-physician length-of-stay gap, and general experience with a 10 percent decline; however, experience does not significantly narrow the preventable hospitalization gap. Second, information acquisition: NPs order more consults, CT scans, and X-rays, consistent with compensating for lower diagnostic skill. Third, prescription thresholds: NPs reduce opioid prescribing by 20 percent and raise antibiotic prescribing by 6.3 percent, consistent with threshold adjustment under asymmetric error costs, but downstream outcomes are not improved correspondingly.

Q: What do prescription patterns and downstream outcomes reveal about NP diagnostic skill? A: NPs prescribe fewer opioids yet patients treated by NPs obtain similar downstream opioid use disorder rates; NPs prescribe more antibiotics yet patients treated by NPs have higher rates of return visits with infections. This pattern is consistent with NPs exhibiting higher rates of both false positives and false negatives, not merely adjusted thresholds, suggesting genuinely lower diagnostic skill rather than threshold differences alone.

Q: What do counterfactual cost calculations show? A: Allocating one quarter of ED patients to NPs raises non-wage spending by $197 million per year to the VHA; after accounting for NP wages being half of physician wages (approximately $120,000 versus $240,000 per year), net cost is still $129 million per year. Restricting NP deployment to the least-complex quarter of cases reduces net spending to approximately one-fifth of this amount, illustrating that targeted case assignment substantially improves NP cost-effectiveness.

Q: How large is within-profession productivity variation relative to between-profession differences? A: The interquartile range in annual spending attributable to provider productivity within each profession is approximately $900,000, roughly three times the mean annual spending difference between the average NP and the average physician. A randomly chosen NP outperforms a randomly chosen physician in up to 38 percent of random pairs. The authors conclude that, despite stark differences in training and selection between professions, within-profession variation dominates.

Q: Is individual provider productivity reflected in wages or case assignment within professions? A: Within each profession, provider productivity shows essentially no relationship with wages or with the complexity of assigned cases. This contrasts sharply with between-profession patterns, where professional class strongly predicts both wages (NPs earn approximately $120,000 per year versus $240,000 for physicians) and assigned case complexity. The authors interpret this as evidence of informational and organizational frictions in recognizing individual productivity within professional classes, and note that professional class is a far stronger predictor of pay and case assignment than is individual productivity.

Q: How do complier characteristics relate to the broader patient population? A: Compliers — cases whose provider type is determined by the instrument — are healthier than the average case: younger, with fewer comorbidities, fewer prior inpatient stays, and lower predicted mortality. Never-takers are riskier than the average case. There are no always-takers since patients cannot be assigned to NPs on days when no NPs are on duty.

Q: How does this paper relate to the literature on NP scope-of-practice laws? A: The scope-of-practice literature estimates general-equilibrium effects of allowing NPs greater autonomy, including labor reallocation between professions. This paper instead estimates the partial-equilibrium causal effect of assigning a patient to an NP versus a physician, holding the broader labor market fixed. The two literatures are complementary: the heterogeneity findings here suggest that scope-of-practice expansions may be more beneficial in lower-complexity primary care settings where the NP-physician performance gap is smaller.

Q: What are the policy implications of the findings? A: Three implications are highlighted. First, the efficiency of using NPs depends critically on case assignment: deploying NPs on the least-complex cases reduces net costs to approximately one-fifth of indiscriminate deployment. Second, the substantial overlap between NP and physician productivity distributions provides support for NP use in less complex settings even within the ED context. Third, within-profession productivity variation far exceeding between-profession differences suggests that individual-level productivity assessment, rather than professional class, may be a more accurate guide to case assignment and compensation.

Quasi-experimental variation in NP availability: The identification strategy exploits day-to-day variation in the number of NPs scheduled to work in a given VHA ED, conditional on ED-by-time-category fixed effects, as an instrument for whether a patient is assigned to an NP versus a physician. Schedules are set months in advance, rendering the NP count orthogonal to arriving patient characteristics conditional on those fixed effects.

30-day preventable hospitalization: A standardized quality-of-care outcome defined by the Agency for Healthcare Research and Quality, measuring hospitalizations occurring within 30 days of ED discharge that are classified as preventable given adequate prior outpatient management. Used by the paper as the primary downstream health outcome beyond the ED visit itself.

Elixhauser comorbidities: A set of 31 binary indicators for chronic conditions (e.g., cancer, diabetes) based on medical histories in the prior 365 days, used in this paper to measure and stratify case complexity into quartiles for heterogeneity analysis.

Productivity distributions within professions: Provider-specific productivity estimates derived from a just-identified IV model that instruments assignment to individual providers by indicators for on-duty providers, then deconvolved into underlying distributions using the Efron (2016) and Kline-Rose-Walters (2022) method. These distributions characterize the spread of productivity within each professional class, separate from measurement error.

Prescription threshold adjustment: The mechanism, formalized in Chan, Gentzkow, and Yu (2022), by which providers with lower diagnostic skill optimally adjust treatment thresholds in response to asymmetric costs of false-positive versus false-negative errors. In this paper’s application, NPs lower the opioid prescription rate (where false positives carry higher costs: addiction and overdose) and raise the antibiotic prescription rate (where false negatives carry higher costs: untreated infection), but downstream outcomes do not improve correspondingly.

Skill-task matching: The organizational economics principle (Acemoglu and Autor 2011) that efficiency requires assigning more complex tasks to higher-skilled workers. The paper documents that between professions, case assignment broadly follows this principle (NPs receive less complex patients on average), but within professions, essentially no matching between individual provider productivity and case complexity is observed.

Full practice authority (VHA, December 2016): The VHA policy that allowed NPs to treat patients independently without physician supervision at VHA facilities, superseding state-level restrictions. This policy change defines the start of the paper’s sample period and establishes the institutional context in which the quasi-experiment occurs, as it removed the requirement for physician oversight that previously constrained NP independence.