The research team, led by Ian Nykaza and Alina Markova of the Dermatology Service at Memorial Sloan Kettering Cancer Center, together with colleagues at Weill Cornell Medical College, examined the work relative value units, or wRVUs, attached to more than thirteen thousand outpatient dermatology visits. The wRVU is the metric at the heart of American physician compensation: it is a standardized number assigned by Medicare to every evaluation and management encounter and every procedure, intended to quantify the physician work involved in caring for a patient. Because salaries, bonuses, and departmental budgets are frequently tied to wRVU production, the metric quietly shapes which clinical services hospitals consider financially sustainable.

To build their picture, the researchers conducted a retrospective review of 13,534 outpatient visits occurring between April 1, 2022, and April 1, 2023. They deliberately excluded inpatient consultations and procedure-only encounters, focusing instead on the routine outpatient clinic work that constitutes the bulk of subspecialty dermatology practice. Each visit was assigned to one of three subspecialty categories: cutaneous oncology, which covers skin cancers and premalignant lesions; cutaneous lymphoma, a rare group of cancers that present in the skin and require intricate diagnostic reasoning; and oncodermatology, the rapidly growing field that manages dermatologic adverse events triggered by modern cancer therapies such as checkpoint inhibitors and targeted agents. For every visit, the team tallied the wRVUs from evaluation and management codes and from any associated current procedural terminology codes.

The headline finding was a geometric mean of 1.76 wRVUs per visit across all three subspecialties, with a 95 percent confidence interval of 1.73 to 1.77. That figure may sound abstract, but in the grammar of American billing it signals consistently high-intensity encounters. The analysis of visit complexity levels makes the point even more clearly. Just over half of all visits, 51.6 percent, were billed at evaluation and management level 3, and nearly 40 percent, 39.9 percent, reached level 4. Oncodermatology stood out even within this already demanding mix: 53.7 percent of its visits were level 4 and 10.7 percent reached level 5, the highest complexity tier in the coding system. In practical terms, most patients walking into these clinics presented problems that required extensive data review, careful medical decision-making, and substantial physician time.

Perhaps the most consequential technical detail in the study is where the wRVUs actually came from. The majority were generated by evaluation and management codes rather than by procedures. This distinction matters because the economics of dermatology have long been dominated by procedural work, such as biopsies, excisions, and Mohs micrographic surgery, which generate robust technical revenue. Cognitive visits, by contrast, have historically been undervalued in the fee schedule, a disparity that prompted significant revisions to evaluation and management reimbursement in recent years. A subspecialty practice whose value derives mostly from cognitive work therefore looks weaker on a traditional revenue spreadsheet even when the clinical intensity per hour is high, a mismatch the authors argue deserves institutional attention.

The patient population behind these numbers underscores why the work is so complex. The 7,589 patients in the cohort had a mean age of 58.7 years, and by definition nearly all were navigating cancer or its aftermath. Cutaneous lymphoma patients often require serial skin examinations, staging discussions, and coordination with hematologic oncology. Cutaneous oncology patients include individuals with melanoma and high-risk nonmelanoma skin cancers whose surveillance demands meticulous, repeated assessment. Oncodermatology patients present some of the most diagnostically tangled scenarios in modern medicine: rashes, mucositis, and immune-mediated eruptions arising from therapies that cannot simply be stopped, because the drugs causing the skin toxicity may be the patient’s best chance at survival. Prior research cited by the authors has shown that dermatologic adverse events can lead to interruption of anticancer therapy, making rapid, expert dermatologic input a genuine component of cancer treatment rather than an ancillary comfort.

The study’s framing is explicitly financial as well as clinical. The authors write that the high billing intensity observed across cutaneous oncology, cutaneous lymphoma, and oncodermatology may reflect high-complexity, cognitively intensive care, and that these findings support further investigation into the financial sustainability of these subspecialties to justify broader institutional investment. That language reflects a real tension in academic medicine. As the population of cancer survivors in the United States continues to grow, demand for specialized skin care within oncology is rising, yet departments must compete for resources against services whose wRVU output is easier to demonstrate. Quantifying productivity in the standard currency of hospital finance is a step toward making the case that these clinics are not cost centers but high-value engines of complex care.

Methodologically, the study is a short report with clear limits that the authors and readers must keep in view. It captures a single year at a single tertiary cancer center, an institution whose case mix, coding practices, and payer environment may differ substantially from community oncology practices or general dermatology clinics. The geometric mean was used instead of the arithmetic mean, a statistically sensible choice given the right-skewed distribution of wRVU values, where a small number of very high-billing visits can distort averages. The researchers also compared the sources of wRVUs across the three subspecialties, providing a granular view of whether value came from office visits, procedures, or both. No new datasets were generated or analyzed beyond the study itself, and the work was supported in part by a National Cancer Institute cancer center support grant, with institutional review board approval at Memorial Sloan Kettering.

What makes the findings resonate beyond dermatology is the broader question they raise about how medicine values thinking. The wRVU system was designed to measure physician work, but its application has repeatedly revealed blind spots, particularly for specialties whose contribution is diagnostic reasoning, care coordination, and the prevention of treatment interruptions rather than the performance of billable procedures. As cancer therapies become more powerful and more toxic, the cognitive load carried by supportive care specialists grows in parallel. A patient whose severe eczematous eruption is correctly managed may keep receiving a therapy that adds years to their life, an outcome no current billing code fully captures.

For now, the study stands as a quantitative baseline. It documents that at one major cancer center, subspecialty dermatology generates high wRVU intensity per visit, driven overwhelmingly by evaluation and management work and concentrated at the upper complexity tiers. Whether that pattern holds across other institutions, how it translates into actual revenue after costs, and how administrators weigh cognitive intensity against procedural volume remain open questions the authors explicitly flag for future research. But the message to hospital leadership is difficult to ignore: the dermatologists managing the skin complications of cancer care are performing some of the most complex outpatient work in the building, and the numbers now exist to prove it.

Subject of Research: Work relative value unit generation and visit complexity in subspecialty outpatient dermatology at a tertiary cancer center

Article Title: Landscape of outpatient dermatology work relative value units (wRVUs) at a tertiary cancer center

Article References: Landscape of outpatient dermatology work relative value units (wRVUs) at a tertiary cancer center. (n.d.). https://doi.org/10.1007/s00403-026-04901-3

Image Credits: AI Generated

DOI: 10.1007/s00403-026-04901-3

Keywords: dermatology, oncodermatology, cutaneous oncology, cutaneous lymphoma, work relative value units, wRVU, visit complexity, evaluation and management, billing intensity, cancer care, healthcare delivery, financial sustainability

The research team, led by Ojarotade Adegbola of the University of Ilesa in Nigeria, together with Adeleye Ebenezer Oloniluyi and Dayo Benedict Olanipekun of Ekiti State University, analyzed secondary panel data from 2001 to 2023 for countries ranging from Nigeria and Kenya to Botswana, Egypt and Morocco. Their measure of healthcare quality was deliberately stark: the infant mortality rate, defined as the number of infant deaths for every 1,000 live births. To capture the extent of medical brain drain, they used physicians’ density per capita, a barometer of how many doctors are actually available to serve a given population. When physicians leave, that density falls; when they stay, it rises.

The econometric machinery behind the study was a two-step system Generalized Method of Moments estimator, a dynamic panel technique prized for its robustness to endogeneity, heteroscedasticity and autocorrelation. Because infant mortality tends to persist over time, the researchers included the lagged infant mortality rate as a regressor, and its coefficient of 0.911 confirmed strong temporal persistence. The Arellano-Bond tests found no problematic serial correlation in the residuals, while Sargan and Hansen statistics confirmed the validity of the instruments, meaning the estimated relationships are unlikely to be statistical artifacts.

The headline finding is strikingly concrete. A 1 percent rise in physicians’ density per capita, the result of doctors staying rather than migrating, was associated with a 0.290 percent drop in infant deaths, statistically significant at the 1 percent level. Read in reverse, the implication is equally stark: every percentage-point erosion of physician availability through emigration translates into a measurable increase in infant mortality. In other words, the loss of a single cohort of migrating doctors is not merely an administrative inconvenience; it shows up, within the data, in the number of babies who do not survive their first year.

The control variables told a coherent story about what else shapes survival. A 1 percent increase in gross domestic product per capita cut infant deaths by 0.150 percent, significant at the 10 percent level, suggesting that economic growth matters most when it trickles down into genuine access to facilities and medicines. Domestic general government health expenditure carried a coefficient of −0.044, significant at the 1 percent level, confirming that public investment in health saves infant lives, provided spending is shielded from corruption. Remittances showed the expected negative sign at −0.025 but fell short of statistical significance, hinting that money sent home by the diaspora is largely channeled into construction and consumption rather than directly into child survival.

One result startled the researchers. Government effectiveness, scored between −2.5 for weak governance and 2.5 for strong governance by the World Bank’s Worldwide Governance Indicators, returned a positive coefficient of 1.900, meaning that a rise in government ineffectiveness was associated with a 1.9 percent increase in infant deaths. The finding underscores the authors’ central warning that health outcomes depend not only on how many doctors a country has, but on whether its institutions can deliver public services competently, free of political interference and waste. A well-staffed hospital in a poorly governed state, the data suggest, still fails its smallest patients.

The scale of the underlying shortage is difficult to overstate. According to figures cited in the study, sub-Saharan Africa has roughly 2 doctors and 11 nurses per 10,000 patients, compared with 19 doctors and 49 nurses in the Americas and 32 doctors and 78 nurses in Europe. Africa carries more than 24 percent of the global burden of disease but commands only 3 percent of the world’s health workers. A World Health Organization report cited by the authors found that 55 countries now fall below the threshold of 49 healthcare workers per 10,000 people, and 37 of them are African, an increase from 47 such countries in 2020, driven by surging demand for health workers in wealthy nations and the lingering aftershocks of COVID-19.

The study is anchored in the classic push-pull theory of migration, first formalized in the nineteenth century by E.G. Ravenstein. Push factors include poor remuneration, inadequate working conditions, insecurity and limited career progression at home; pull factors include higher salaries, modern equipment, professional development and political stability abroad. Migrants, in this framing, perform an implicit cost-benefit calculation, relocating when the economic pull of the destination outweighs the push of their home country. The theory explains why the exodus persists despite its obvious costs: individual doctors are acting rationally, even as the aggregate effect drains their nations of irreplaceable human capital. Critics note that the framework understates the role of deliberate recruitment by wealthy countries’ health systems, which actively fill staffing gaps with talent trained at poor countries’ expense.

The study distinguishes itself from earlier work by operating at the macro level. Prior investigations, including survey-based studies in Enugu State, Benin City and Akwa Ibom State in Nigeria, found negative effects on treatment outcomes and service delivery but were confined to single institutions or states. One influential cross-country analysis covering 188 countries between 2000 and 2015 found that medical brain drain could paradoxically coincide with falling child mortality, but it used static estimation and did not isolate African countries. By restricting the panel to 35 African nations, extending the timeline to 2023, introducing remittances and government effectiveness as regressors, and applying dynamic GMM estimation, the new study closes those gaps and delivers a sharper, Africa-specific picture.

The policy prescriptions are direct. African governments, the authors argue, must raise salaries to levels that allow health workers to live decently, guarantee job security and timely retirement benefits, expand career development pathways, and increase annual medical school enrollment, ideally in partnership with international institutions, to offset continuing losses. They call for economic growth that genuinely reaches ordinary citizens, sustained and streamlined diaspora remittance flows, larger national budget allocations to health, and vigilant, corruption-free oversight of the health sector. The authors also frame the problem as a shared responsibility: Western employers who recruit African doctors, as the British Medical Association has acknowledged, are effectively importing trained personnel that the world’s poorest countries paid to produce. If the recommended measures are implemented seriously, the study concludes, the brain-drain syndrome can be curtailed, physician densities can recover, and the continent’s fragile health systems can begin delivering the quality of care that African infants, and all African patients, deserve.

Subject of Research: Quantitative analysis of how the emigration of skilled medical workers affects infant mortality and quality healthcare delivery in 35 African countries from 2001 to 2023.

Article Title: Medical brain-drain and quality healthcare delivery in African countries

Article References: Adegbola, O., Oloniluyi, A. E., & Olanipekun, D. B. (2026). Medical brain-drain and quality healthcare delivery in African countries. Discover Global Society, 4(1), Article 250. https://doi.org/10.1007/s44282-026-00556-7

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00556-7

Keywords: medical brain drain, Africa, infant mortality, healthcare delivery, physician density, remittances, government effectiveness, health expenditure, push-pull migration theory, system GMM, health workforce crisis, Discover Global Society

The researchers set out to map where, in the machinery of modern healthcare delivery, AI-related harms actually occur. Rather than treating AI as a single black box that a doctor either accepts or rejects, they categorized the cases according to the specific clinical tasks physicians perform: diagnosing conditions, ordering and interpreting tests, planning treatment, triaging patients, documenting and communicating information, and coordinating care across institutions. The distribution was revealing. Harms clustered not only around diagnostic tools — the category that dominates public imagination and much of the regulatory conversation — but also around administrative and coverage systems, including AI used by insurers to deny or curate care, and screening tools deployed by state agencies to flag families for investigation.

That breadth matters because it exposes a structural blind spot in both technology design and legal doctrine. Much of the existing scholarship on AI liability in medicine has centered on a deceptively simple question: if an AI tool outperforms a doctor, does reliance on it constitute negligence, or does deviation from it? Prior work, including widely cited analyses in JAMA and law reviews, has debated whether physicians could face liability for ignoring a competent algorithm or for deferring to a flawed one. Mansi and Riedl argue that this physician-centered frame is too narrow to describe what actually happens when patients are harmed. An insurance company’s algorithm may terminate home-care coverage; a state screening tool may incorrectly flag a parent for child-welfare investigation; a triage system may misdirect emergency care. In each scenario, the patient’s injury emerges from decisions distributed across an organization, with the physician often having little visibility into — or control over — how the AI was configured, trained, deployed or applied.

The legal cases the researchers analyzed come from a meticulous triangulation of three public databases: the Database of AI Litigation maintained at George Washington University Law School, the Health Litigation Tracker, and the AI, Algorithmic and Automation Incidents and Controversies repository, a community-maintained archive of documented harms. As of February 2025, all three resources are freely accessible online, and the researchers referenced individual cases through unique identifiers, centering documented harms rather than the specific systems involved. The case-study approach allowed the authors to reconstruct, case by case, how harm materialized, which stakeholders were positioned between the patient and the algorithm, and what barriers the patient encountered in seeking recourse.

Those barriers, the study finds, are both legal and technological, and they reinforce one another. On the legal side, liability structures remain tethered to a model of medical malpractice that presumes a human decision-maker with enough information and discretion to have acted differently. When an algorithm’s output is embedded in a payer’s coverage workflow or a state agency’s screening pipeline, a patient’s lawyer must identify which entity’s conduct was negligent, prove causation, and often overcome trade-secret protections that keep the model’s logic opaque. On the technological side, the tools themselves are rarely designed for contestability. Explanations, where they exist, are built for clinicians — and even for clinicians, a substantial body of human-computer interaction research shows that explainability features are frequently underused, misunderstood or shaped more by regulatory checkbox than by genuine decision support. They are almost never built for the people who most need them when things go wrong: patients and their advocates.

Here the study makes its most provocative move. Mansi and Riedl argue that lawyers working for patients should be recognized as legitimate users of AI systems — and, in a sense, as intermediaries whose needs deserve to shape the design of medical AI from the ground up. If an explainability interface cannot help a patient’s legal team understand which stakeholder made which consequential decision, what data the model relied on, and where in the pipeline the error propagated, then the tool has effectively insulated everyone but the patient from scrutiny. The authors connect this to a growing research program on legally-informed explainable AI, which aims to produce explanations aligned with the evidentiary demands of legal proceedings rather than with the aesthetic of transparency that currently dominates industry practice.

The researchers also draw a pointed lesson from documented incidents involving coverage algorithms. Reporting on how Medicare Advantage plans have used AI tools to cut off care for seniors, alongside legal scholarship on regulating healthcare coverage algorithms, illustrates the pattern the study identifies: harms arise at the intersection of commercial incentives, algorithmic automation and fragmented oversight. A patient denied post-acute care by an algorithmic coverage tool may appeal through administrative channels, sue, or simply suffer silently. Each path demands different evidence, different expertise and different institutional access — resources that are unevenly distributed among the very populations most likely to be harmed.

What can be done? The authors propose reform on two fronts. First, liability structures should evolve to reflect the distributed reality of AI-mediated care. They point to legal scholarship exploring theories such as the common enterprise theory of liability, which could allow responsibility to be shared across the ecosystem of actors — developers, payers, facilities, regulators — that jointly shape how a tool affects patients. Distributed governance frameworks for medical AI, and regulatory thinking that treats software as a medical device within a whole system rather than as a standalone artifact, offer templates for how accountability could be allocated more realistically. Second, AI tools for healthcare should be designed for contestability by default: systems should log which stakeholders shaped their outputs, generate explanations usable in legal contexts, and support advocates — including patients’ lawyers — in reconstructing what happened when harm occurs. Contestable AI, an emerging design philosophy in human-computer interaction, offers concrete principles: make decisions reversible, make the grounds of decisions accessible, and make the pathway to human review visible to affected people.

The stakes of getting this wrong extend beyond individual lawsuits. If patients cannot seek recourse, there is no corrective feedback signal pushing developers and deployers to fix flawed systems; courts become the only place where the accumulated evidence of algorithmic failure can surface, and only for those few patients wealthy or persistent enough to get there. Conversely, if accountability is designed into the system — technologically through auditable, contestable tools, and legally through liability frameworks that track the actual distribution of decision-making power — then recourse becomes possible without requiring every patient to become a litigant. The study’s authors, whose work was supported in part by the National Science Foundation, frame this as a shift from physician-centered to patient-centered accountability, a reorientation that demands new collaboration between lawyers, technologists, clinicians and regulators.

For a healthcare system rapidly absorbing AI into triage, diagnosis, documentation and coverage decisions, the message of the litigation record is uncomfortable but clear: the technology is arriving faster than the accountability structures needed to govern it, and the people bearing the consequences are the ones the current framework serves least. Mansi and Riedl’s analysis of 31 cases turns courtroom silence into a design specification — a map of where AI harms happen in healthcare delivery, and a blueprint for tools and laws that could finally let patients fight back.

Subject of Research: Analysis of US litigation involving medical AI tools and patient-centered accountability for AI-related harms in healthcare delivery.

Article Title: Implications of current litigation on the design of AI tools for healthcare delivery and related legal frameworks

Article References: Mansi, G., & Riedl, M. (2026). Implications of current litigation on the design of AI tools for healthcare delivery and related legal frameworks. Nature Health. https://doi.org/10.1038/s44360-026-00186-y

Image Credits: AI Generated

DOI: 10.1038/s44360-026-00186-y

Keywords: medical AI, healthcare litigation, AI liability, patient recourse, explainable AI, contestable AI, healthcare delivery, algorithmic harms, coverage algorithms, legal frameworks, patient-centered accountability, Nature Health