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A patient calls the clinic the morning of their appointment. Their ride canceled. Again. The scheduler offers to rebook, but the patient hesitates — they've already canceled twice this month for the same reason, and they're starting to wonder if the clinic thinks they just don't want to come in. They do. They just can't get there. I've heard versions of this story enough times recently that it stopped feeling like a string of coincidences and started feeling like a pattern. Transport companies are declining Medicaid and Medicare rides because the reimbursement doesn't cover the cost of fuel to get there. No-shows and cancellations are climbing. And in a separate but oddly parallel thread, research coordinators are reporting that study participants — people who once reliably showed up for their visits — are skipping appointments because the incentive payment no longer covers what it costs to drive there. These are two different systems, two different funding mechanisms, two different sets of patients. But they're failing for the same underlying reason, and I don't think that's a coincidence. How a flat rate breaks under a variable cost Non-emergency medical transportation, or NEMT, is supposed to be the safety net that gets Medicaid and Medicare beneficiaries to dialysis, infusion, oncology follow-up, and the dozens of other appointments that can't happen by telehealth. The way these trips are usually priced is a base rate plus a per-mile mileage fee, and that mileage fee is explicitly built to reflect local fuel prices, vehicle maintenance, and regional economic conditions. Reimbursement rates themselves vary enormously by state — the same wheelchair-accessible trip might pay around $100 in one state and roughly a third of that in another, because federal law requires states to provide NEMT but leaves the actual payment rate entirely up to them. That structure works fine as long as the underlying cost of driving stays roughly where it was when the rate was set. It does not work when fuel prices climb faster than the rate gets revised. A transport company running on Medicaid mileage reimbursement doesn't have the option of just absorbing the loss trip after trip — they stop taking the trips. Which is, anecdotally, exactly what's happening. The data we already had Here's the part that surprised me a little: we didn't need a fuel crisis to know transportation barriers cause missed appointments. That literature already exists, and it's not small. A frequently cited estimate puts the number at roughly 5.8 million Americans missing or delaying medical care annually because of transportation barriers, concentrated in rural and underserved urban areas where transportation options are already limited. In one study of caregivers in Houston, an inability to find a ride caused at least one missed appointment in a quarter of the sample. A systematic review and meta-analysis of interventions aimed at exactly this problem — vans, bus vouchers, rideshare — found they meaningfully reduced missed appointments, though the evidence on whether that translated into better health outcomes or lower costs was too thin to say for sure. So the mechanism by which "can't get a ride" becomes "missed dialysis session" was already well established. What's new isn't the mechanism. It's the scale and speed at which fuel prices are stressing a system that was already running close to the edge. The same problem, wearing a different badge The research side of this is structurally different but rhymes uncomfortably well. Clinical trial and study compensation has its own literature on travel reimbursement, and it's clear on one point: covering travel costs isn't a perk, it's often the thing standing between "this person can participate" and "this person can't afford to." One review of payment practices noted that travel costs remain one of the most significant barriers to clinical trial participation, particularly for low-income participants. Separately, researchers studying recruitment and retention have argued that travel reimbursement is an appropriate and valuable incentive precisely because, without it, participation becomes a luxury good — available to people who can absorb the cost of getting there, and closed to everyone else. If incentive payments were calibrated to cover a $15 round trip in gas and now the actual cost is closer to $25, that calibration has quietly become a barrier, even though the dollar amount on paper hasn't changed. The people most likely to drop out under those conditions are, predictably, the people for whom that gap matters most — which is its own quiet threat to the diversity and generalizability of the data we're collecting. What I can't tell you yet I want to be honest about the limits of what I'm describing. There is, as far as I can find, no published literature yet on this specific moment — on fuel prices rising fast enough to push NEMT providers out of Medicaid and Medicare contracts, or on research incentive payments failing to keep pace with gas prices in real time. What I have is a well-documented mechanism (transportation barriers cause missed appointments and lower trial retention) colliding with an acute, recent stressor (fuel costs outpacing reimbursement) that hasn't been studied yet because it's still happening. It would be tidier to end this with a clear causal claim and a clean policy fix. I don't think I'm entitled to either yet. What I can say is that two systems I don't normally think about together — clinical transportation logistics and research recruitment economics — are both showing the same symptom right now, and that symptom is patients and participants disappearing from the schedule not because they don't want to be there, but because the math of getting there no longer works. A structural irony, if you're looking for one The patients most likely to need frequent transportation-dependent care — dialysis, transfusion, infusion therapy, complex follow-up — are, by definition, the ones who can least afford for this particular gap to widen. We built a system where access to care depends on a per-mile rate someone set years ago, in a different fuel market, and we're now finding out what happens when that assumption quietly stops holding.

In 2008, an FDA advisory panel sat down with a meta-analysis that pooled thirteen randomized trials of cell-free hemoglobin-based oxygen carriers — HBOCs, for short — and found that, as a class, these products increased the risk of myocardial infarction and death compared to controls. Within the year, the FDA had effectively frozen HBOC development in the United States. Almost two decades later, the freeze hasn’t really lifted. One of the products caught in it, Hemopure, has spent that entire time legally available in South Africa, used there since 2001 for acute surgical anemia, with no comparable reckoning. That gap is the interesting part. Not whether Hemopure works — it does, in the narrow sense of carrying oxygen — but why a product can be standard of care in Johannesburg and investigational-only, accessible solely through expanded access protocols, in Boston. The Pitch Hemopure (HBOC-201) is purified, glutaraldehyde-polymerized bovine hemoglobin, suspended in a balanced electrolyte solution and packaged in a 250 mL bag. It solves, on paper, two of transfusion medicine’s oldest structural problems at once. First, compatibility: there’s no antigen to react to, so no type and screen, no crossmatch, no antibody workup — a feature that matters enormously for a patient with a complex alloantibody history, or a Jehovah’s Witness declining allogeneic blood, or a combat medic with no time and no lab. Second, supply: it’s shelf-stable at room temperature for years, not the 42 days we get out of refrigerated red cells. No cold chain, no expiration anxiety, no donor recruitment problem. It is, in other words, exactly the product blood banking has wanted since the first synthetic oxygen carrier was proposed. Which is part of why its failure to gain US approval stings more than a typical drug rejection — this isn’t a marginal improvement on an existing therapy. It’s a different category of solution to a problem we still haven’t solved. What the Meta-Analysis Actually Said The 2008 Natanson analysis, published in JAMA, didn’t study Hemopure alone. It pooled data across five distinct molecules — HemAssist, PolyHeme, Hemolink, Hemopure, and Hemospan — spanning surgical, trauma, and stroke populations treated between 1980 and 2008. The conclusion was stark: roughly a 30% increase in risk of death and nearly a threefold increase in risk of myocardial infarction across the pooled trials. The FDA responded by putting HBOC research as a class on clinical hold, and pharmaceutical interest in the space mostly evaporated. The proposed mechanism made biological sense and still does: free hemoglobin outside the protective confines of a red cell membrane scavenges nitric oxide, the molecule responsible for vasodilation. Scavenge enough of it and you get vasoconstriction, hypertension, and — plausibly — myocardial ischemia. This isn’t a manufacturing defect specific to one company. It’s closer to a property of cell-free hemoglobin itself, which is a much harder problem to engineer around. The Harder Question Here’s where I think the story gets genuinely uncomfortable, and where I have a hard time being charitable to the paper that started all of it. Natanson and colleagues pooled thirteen trials of five chemically distinct molecules — different polymerization strategies, different patient populations, different routes and doses, trauma and elective surgery and stroke trials run across nearly three decades — into a single composite risk estimate, and reported finding no significant statistical heterogeneity across that grab-bag. I find that more suspicious than reassuring. Getting a clean, homogeneous-looking signal out of five drugs that don’t share a structure, in populations that don’t share a baseline ischemic risk, is exactly the kind of result that should prompt a second look at the methodology rather than a press release. Several independent groups thought so too: JAMA ran six separate rebuttal letters in the same issue — from South African clinicians with the largest real-world experience with Hemopure, from the manufacturers, from trauma surgeons, from bioethicists — which is not a normal amount of pushback for one meta-analysis to generate. Natanson’s own paper acknowledged that the authors had struggled to obtain complete trial data directly from the companies, meaning the headline number was built partly on data the authors themselves described as incomplete. It also doesn’t help that the senior author, Sidney Wolfe of Public Citizen’s Health Research Group, had already petitioned the FDA over HBOC trial safety back in 2006 — two years before he co-authored the analysis that became the FDA’s rationale for freezing the entire class. None of that automatically makes the conclusion wrong. But a paper with this many independent critics, this much acknowledged missing data, and an author who’d staked out the answer in advance is not the kind of evidence I’d want sitting alone at the foundation of a two-decade regulatory freeze — and yet here we are, two decades later, and it still is. And then there’s the South Africa and Russia question, which nobody seems eager to sit with for very long. If the safety signal were straightforwardly damning, you’d expect those approvals to have been revisited over twenty-plus years of real-world use. They haven’t been. Either the signal doesn’t replicate cleanly outside the specific trial populations that generated it, or post-marketing surveillance in those countries simply isn’t rigorous enough to have caught it — and I genuinely don’t know which of those is true. Both possibilities should make a transfusion medicine physician uneasy, just in different directions. Meanwhile, expanded access use in the US has quietly continued for patients with life-threatening anemia and no other option — mostly Jehovah’s Witnesses and patients with antibody profiles that make compatible blood functionally unobtainable. Case series from these programs report real patients surviving severe anemia they likely wouldn’t have survived otherwise, alongside the same cardiovascular signal the trials raised. The regulatory caution and the individual patient calculus are not measuring the same thing, and I don’t think they’re supposed to converge. A population-level hold protecting against a class-wide signal can be correct and still be the wrong answer for the specific patient in front of you with no other option. Sitting with that tension honestly is harder than resolving it in either direction. Where It Sits Now Hemopure remains investigational in the US, available only through expanded access or clinical trial. HbO2 Therapeutics, the company that now holds the product after Biopure’s bankruptcy and a subsequent ownership chain, has kept it alive primarily through that compassionate-use pathway and continued approval in South Africa and Russia. The broader HBOC field never really recovered momentum after 2008; most of the other products named in the Natanson analysis are gone entirely. Hemopure is something closer to a survivor than a success — still infused, still studied in scattered case reports, still without a clear path to a US indication. There’s a newer thread worth watching: small case literature on HBOC-201 for ischemic rescue in cardiology and vascular contexts, distinct from its original blood-substitute framing. Whether that becomes a real niche or stays anecdotal is an open question, and I’m not going to pretend I know which. I don’t think this is a story with a villain. The FDA did what regulatory agencies are supposed to do when a meta-analysis raises a mortality signal across a drug class. But twenty years on, with the same product still quietly saving the occasional patient who has no other option, and still in routine use on two other continents, I find myself less sure than I’d like to be about whether the caution and the evidence are still pointing in the same direction — or whether we’re applying a 2008 verdict to a 2026 question.

The system is collapsing. Before we try to save it, we should ask whether it was working. Something Has Changed Something has changed in academic publishing. Papers I submit take longer to get reviewed than they used to. Desk rejections — the kind where a paper doesn’t make it out to reviewers at all — feel more common. When a review does come back, it sometimes arrives months after submission, accompanied by an apology from an editor who clearly struggled to find anyone willing to assess the manuscript. I’ve wondered if I’m imagining it, or if my experience is just narrowly my own. It isn’t. The Infrastructure Is Fraying Simberloff and colleagues recently published 21 years of editorial data from Biological Invasions — a granular, longitudinal dataset that makes the pattern hard to argue with. In 2003, more than 60% of invited reviewers accepted. By 2023, that number had fallen to just below 40%. Decline rates rose to match. The lines have now converged: for every scientist who says yes, one says no. If the trend holds, declines will soon outpace acceptances. This is one journal, one field. But a 2018 Publons survey found something consistent across all scientific disciplines: 10% of reviewers complete more than half of all reviews. The system is not failing uniformly. It is being held together by a small, overloaded minority while everyone else declines — and, increasingly, doesn’t bother explaining why. In the Biological Invasions data, the most common reason given for declining is being too busy, a response that has grown more frequent over time. Lack of expertise is also frequently cited. But roughly half of all decliners give no reason at all. There are no consequences for saying no, so scientists have stopped feeling the need to justify it. But Was It Ever Working? Before we treat this as an unambiguous crisis, it’s worth asking what exactly we’re losing. Peer review has long been treated as the quality-control mechanism of science — the filter that keeps bad research out of the record. That assumption deserves scrutiny. The psychologist Adam Mastroianni has written compellingly about peer review as a failed experiment. The evidence he marshals is uncomfortable. Studies in which researchers deliberately inserted major errors into manuscripts — things like misrepresented study designs, unsupported conclusions, obvious discrepancies between data and graphs — found that reviewers caught somewhere between 25 and 30% of them. Not 25 to 30% of minor quibbles. Major methodological flaws. Most of what reviewers are supposed to catch, they miss. The fraud data tell the same story. If peer review were functioning as a rigorous filter, we would hear about fraud attempts stopped at the gate. We don’t. Almost every high-profile case of scientific fraud begins with a paper that passed review and was published. The detection comes later — from a lab member, a methodologist, someone on the internet who noticed something odd about the error bars. Review did not catch it. Post-publication scrutiny did. None of this means peer review does nothing. It probably catches some errors, improves some papers, and deters some bad actors who would otherwise have no barriers at all. But the gap between what peer review promises and what it delivers is substantial. We have been running on faith more than evidence. The Bargain We Made The deeper problem is what got built on top of peer review’s assumed reliability. Hiring committees treat publication in peer-reviewed journals as a proxy for scientific quality. Grant agencies use it as evidence of track record. Clinicians — and I count myself here — use peer-reviewed literature to make decisions about patient care. The peer-reviewed label became a kind of certification, and institutions downstream of the scientific record built their practices around it. That certification was always shakier than it looked. But the response, broadly, has been to defend peer review rather than examine it — to argue that more of it, or better-resourced versions of it, would fix the problem. The collapse now underway is forcing a different question: not how do we sustain peer review, but what do we actually need from it, and is there a better way to get there. Why Nothing Will Change Here is the detail from the Simberloff paper that has stayed with me. The editors-in-chief of Biological Invasions — the people running the journal, watching decline rates climb year after year, doing the actual work of recruiting reviewers into an increasingly reluctant pool — asked Springer Nature, their own publisher, for reviewer incentives. They asked multiple times. Springer Nature declined. This is not surprising. It is clarifying. Springer Nature collects subscription fees, article processing charges, and the commercial value of a prestigious catalog, all sustained by the unpaid labor of reviewers and the prestige conferred by the peer-review label. There is no version of that business model that benefits from fundamental reform. The current system, however dysfunctional, is profitable. Incentives to change it would have to come from somewhere else. This is also part of a larger pattern. Park and colleagues’ 2023 analysis of 45 million papers spanning six decades found a steady decline in disruptive science — work that challenges existing frameworks rather than incrementally extending them. The same incentive structure that rewards volume over depth is now degrading the mechanism that was supposed to ensure quality. More submissions, fewer willing reviewers, and the institutions profiting from the system declining to invest in its sustainability. We Need a New Model There are alternatives being tried. Preprint servers like bioRxiv and medRxiv allow rapid dissemination before formal review, with post-publication scrutiny doing some of the work that pre-publication review was supposed to do. Open peer review, where reviewer identities and comments are made public, attempts to introduce accountability into a process that currently operates without it. Some journals are experimenting with paying reviewers. These are not nothing. But none of them have yet accumulated the institutional weight that peer-reviewed publication carries. Hiring committees still count papers. Grant agencies still look at journals. Clinicians still defer to the peer-reviewed label, even knowing what we know about its limitations. The alternative models exist at the margins while the incumbent system, imperfect and increasingly unsustainable, holds the center. I don’t know what the right model looks like. I don’t think anyone does with confidence. What I do know is that we need one, that the timeline is shorter than it probably feels, and that the people with the resources and infrastructure to build it have spent decades demonstrating they have no intention of doing so. That is the peer review bargain in 2025: a system that over-promised on quality, under-delivered on rigor, is now running out of the volunteers who kept it going, and has no obvious succession plan. Referenced works: Simberloff D et al. (2025). Quantifying reviewer declines in scientific publishing: twenty-one years of data from Biological Invasions 2002–2024. Biological Invasions, 27, 223. https://doi.org/10.1007/s10530-025-03679-1 Mastroianni A. (2022). The rise and fall of peer review. Experimental History. https://www.experimental-history.com/p/the-rise-and-fall-of-peer-review Park M et al. (2023). Papers and patents are becoming less disruptive over time. Nature, 613, 138–144. https://doi.org/10.1038/s41586-022-05543-x

It’s two in the morning. Your pager goes off. A nurse tells you her patient spiked a fever — 38.4°C, up 1.2 degrees from baseline — about two hours into a unit of packed red cells. She stopped the transfusion and is waiting for your call. You work it up. DAT negative. Plasma clear. No hemoglobinuria. No respiratory distress. No hypotension. The patient’s mildly uncomfortable but gets better with acetaminophen. In the morning you write it up: febrile non-hemolytic transfusion reaction, FNHTR, imputability definite. Classic presentation. Before January 2026, that case went into the National Healthcare Safety Network Hemovigilance Module as a reportable adverse reaction. It became one data point in a national count of how often this happens, in which patients, with which products. After January 2026, it goes nowhere. You file it in your own system, call it whatever you call it, and move on. That change is the story I want to tell. What Changed The NHSN Hemovigilance Module has been the national platform for transfusion safety surveillance in U.S. hospitals since 2009. For most of its life, it asked participating facilities to report a broad taxonomy of adverse reactions — twelve defined reaction types across twenty separate forms — classified by case definition, severity, and imputability. Reactions that were possibly, probably, or definitely related to a transfusion were required. Every FNHTR. Every allergic reaction above the minor threshold. Every delayed hemolytic. Every hypotensive reaction. The idea was to capture the full landscape of transfusion-associated harm. Version 3.0, released in January 2026, makes a dramatic cut. Required reporting now covers exactly four reactions: transfusion-associated circulatory overload (TACO), transfusion-related acute lung injury (TRALI), acute hemolytic transfusion reaction (AHTR), and transfusion-transmitted infections (TTI). Everything else — FNHTR, delayed hemolytic, delayed serologic, allergic, hypotensive, transfusion-associated dyspnea, post-transfusion purpura, transfusion-associated graft versus host disease — is now classified as “Other.” The “Other” category is optional. And the CDC has stated explicitly that “Other” data will not be used to calculate rates. Twenty forms became four. Twelve defined reaction types became four, plus a catch-all. What the New Module Gets Right The case for simplification is real. One of the quiet problems with the old module was inconsistent participation. Not every institution had the infrastructure to report reliably. The imputability framework — which required classifying each reaction as definitely, probably, possibly, doubtfully, or not related to the transfusion — was applied unevenly across hospitals, with significant variability in how individual blood banks interpreted and recorded those categories. A national database is only as good as the data going into it, and data entered inconsistently is a form of noise. Fewer required forms means lower burden, and lower burden means better compliance. If the goal is accurate national surveillance of the most dangerous transfusion reactions, it makes sense to focus on the reactions most likely to cause serious harm or death. TACO, TRALI, AHTR, and TTI represent the sharp end of the risk spectrum. They are the reactions you lose patients to. A TTI Rapid Alert form that triggers within 72 hours — a new feature in v3.0 — is a genuinely useful public health tool for catching emerging pathogens in the blood supply before they spread. The new module also adds a TTI Investigation Form with a structured pathway for coordinating between hospitals, health departments, and the CDC. That’s a meaningful improvement in how we respond to the reactions that matter most urgently. What We Might Be Giving Up Here is where I want to slow down. The first loss is data. FNHTR is the most common transfusion reaction we see. Allergic reactions are a close second. Delayed hemolytic transfusion reactions, particularly in patients with sickle cell disease, can be life-threatening. These reactions are now optional to report and will no longer appear in national rate calculations. If you want to know how often FNHTR happens per unit transfused in the United States, or whether that rate is changing, you will not be able to answer that question from NHSN data going forward. The baseline we’ve been building since 2009 is effectively being abandoned for these reaction types. The second loss is resolution. Collapsing eight defined reaction types into “Other” doesn’t make those reactions disappear — it just makes them indistinguishable from one another in the national record. A hypotensive reaction and a delayed serologic reaction both go into the same optional bucket. The third loss is perhaps the most underappreciated. For fifteen years, the NHSN Hemovigilance Module provided something that the field rarely talks about explicitly: a shared vocabulary. The case definitions — FNHTR requires fever ≥ 38°C with a change of at least 1°C from baseline, or chills, within four hours of transfusion cessation; DHTR requires a positive DAT between 24 hours and 28 days with serologic evidence and inadequate hemoglobin rise — were the language everyone agreed to speak. The module’s own protocol disclaimed clinical use of these definitions, and yet every transfusion medicine fellow learned them. Every blood bank used them. Every paper in this field cited them. Those definitions still exist in Section 6 of the new protocol, archived for reference. But they are no longer the required framework for national reporting. Without that institutional anchor, definitional drift will come. Not immediately — the field’s memory is long — but over a decade, as trainees learn from attendings who learned from a protocol that no longer exists in the same form, variability will creep in between institutions, between publications, between how we talk to each other about what a reaction even is. The Honest Landing I don’t think this change is obviously wrong. The logic behind it is defensible, and the improvements to TTI surveillance are real. What I can’t tell you is whether the tradeoffs will be worth it. That answer will take years to emerge, and by the time we know, we will have already lost the data we chose not to collect. That’s the thing about surveillance systems. The cost of narrowing them is invisible at first. You don’t see the data you’re not gathering. You don’t miss the baseline you’re no longer building. The gap only becomes visible later, when someone asks a question about FNHTR rates in 2030 and realizes the answer stopped being tracked in 2026. For now, my FNHTR gets documented in our local system, classified with whatever terminology we happen to use, and counted in no national total. Whether that’s fine — whether the simplification is worth the resolution we gave up — is a question I’m genuinely not able to answer yet. I’m curious whether others in the field see it differently.

The pager goes off. The message reads: “Bloodless surgery consult — patient refusing blood products.” If you are a transfusion medicine fellow and this is your first one, you probably spend a moment staring at your pager wondering exactly what you are supposed to do with that information. You show up, introduce yourself, and are handed a form. It is several pages long. At the top, in clear block letters: WHOLE BLOOD COMPONENTS. Below that, a list of products — red blood cells, platelets, plasma — each followed by two checkboxes. Accept. Reject. It seems simple enough. Then you keep reading. By the time you reach plasma protein fractions, recombinant clotting factors, and thrombopoietin mimetics, you realize this is not a simple form. It is a document that asks a person to think, in advance and in detail, about exactly how much of their own blood — and everyone else’s — they are willing to accept back into their body under duress. The checkboxes are tidy. The clinical reality underneath them is not. Why This Consult Exists The most common reason you will encounter a bloodless surgery consult is a patient who is a Jehovah’s Witness. Members of this faith generally decline transfusion of whole blood and its four primary components — red blood cells, white blood cells, platelets, and plasma — based on a religious interpretation of scriptural passages prohibiting the “taking in” of blood. But the boundaries of that refusal are personal, not prescribed. Individual Jehovah’s Witnesses vary significantly in what they will and will not accept, which is precisely why the form exists and why the consult matters. Not every patient requesting bloodless or transfusion-free care is a Jehovah’s Witness. Some patients have religious objections that are less formalized. Others have philosophical objections to allogeneic blood, concerns about transfusion-transmitted infections, or simply a strong preference to avoid a product they view as high-risk. The label “bloodless surgery” is something of a misnomer — the goal is not zero blood, but zero allogeneic blood. Whether that is achievable depends on the clinical situation, the alternatives available, and what the patient has actually agreed to. The form is the tool that documents that agreement. The conversation is the actual work. The Form, Decoded Walk through the major product categories and the clinical stakes become clearer. Whole blood components — red cells, platelets, plasma, white cells — are the straightforward part. Most patients who have thought carefully about this have already made their decision about these products before you walk in the room. These are the checkboxes they came prepared for. Plasma fractions are where things get philosophically interesting. Albumin is derived from pooled human plasma, fractionated, and heat-treated. Cryoprecipitate is thawed plasma precipitate, rich in fibrinogen and factor VIII. Fresh frozen plasma is essentially unfractionated. A patient might accept albumin but decline FFP, not because they are being inconsistent, but because fractionation changes the product enough to matter to them, even if it does not particularly change the clinical calculus for you. This is not a contradiction you are there to resolve. It is a distinction you are there to understand and document. Autologous techniques — cell saver, acute normovolemic hemodilution, apheresis, dialysis — occupy a fascinating middle ground. Many patients who decline allogeneic blood are entirely comfortable with their own blood being collected, processed through a machine, and returned to them, as long as the circuit remains closed and continuous. The blood never “leaves” them in any meaningful sense. Practically, this means cell saver is often on the table even when packed red blood cells are not, and that distinction matters enormously in a surgical or hemorrhage scenario. Erythropoiesis-stimulating agents, colony-stimulating factors, and thrombopoietin mimetics round out the list. These are pharmacologic scaffolds — tools to build up what the patient has before a major procedure, or to support recovery after one. Some formulations contain albumin as a stabilizer. For some patients, that matters. For others, it does not. You need to know which. The Grey Area Nobody Warns You About: Plasma-Derived Clotting Factors Here is something the form does not make obvious, and that fellows often do not realize until they are standing at the bedside: several of the products in the “clotting factors” section are derived from pooled human plasma. Kcentra — the four-factor prothrombin complex concentrate most of us reach for in warfarin reversal or urgent coagulopathy — is plasma-derived. So is Riastap, the fibrinogen concentrate. Humate-P, which contains both factor VIII and von Willebrand factor, is plasma-derived. These are not recombinant products engineered in a lab. They are fractionated from pooled donor plasma, processed and pathogen-reduced, but fundamentally the same source material as fresh frozen plasma. The processing is different. The origin is not. A brief detour into hemophilia is useful here, because the recombinant versus plasma-derived distinction has a history that most fellows outside of hematology do not fully appreciate. For most of the twentieth century, factor VIII and factor IX concentrates used to treat hemophilia A and B were plasma-derived — pooled from thousands of donors, with all the viral risk that entailed. The consequences in the 1980s were devastating: contaminated plasma-derived concentrates transmitted HIV and hepatitis C to a substantial portion of the hemophilia population before adequate screening and viral inactivation methods existed. That disaster drove the development of recombinant factor products, which began reaching the market in the early 1990s. Today, recombinant factor VIII and factor IX concentrates — including extended half-life versions — are the standard of care for hemophilia in high-income settings. Plasma-derived equivalents still exist and are still used, particularly where recombinant products are less accessible, and in conditions like von Willebrand disease where a plasma-derived product containing both factor VIII and vWF is sometimes preferred. But the field has largely moved on. The relevance for bloodless surgery is this: the products you are most likely to reach for in an acute coagulopathy — Kcentra, Riastap — do not yet have widely available recombinant equivalents. A recombinant fibrinogen concentrate exists in development but is not in routine clinical use. So unlike hemophilia care, which has largely transitioned away from plasma-derived products, the hemostatic toolkit for your typical bleeding surgical patient is still substantially plasma-derived. That gap matters when your patient has declined plasma. Where recombinant options do exist, they matter a great deal. Recombinant factor VIIa (NovoSeven) is produced in baby hamster kidney cells — no human plasma involved. Recombinant factor VIII and factor IX are similarly plasma-free. For a patient whose objection extends to all human blood fractions, these products may be acceptable where plasma-derived concentrates are not. The reverse can also be true: some patients are comfortable with highly processed plasma fractions but draw the line at whole plasma or red cells. You cannot predict which way a given patient will land. The form gives you a framework. The conversation gives you the actual answer. This is one of the more uncomfortable aspects of bloodless surgery medicine: the fellow’s job is not just to document preferences, but to ensure those preferences are genuinely informed. That means being willing to say, politely and clearly, “I want to make sure you know that this product comes from human plasma — is that still acceptable to you?” Most patients appreciate it. Some are surprised. Occasionally, it changes their answer. All of those outcomes are better than the alternative. When the Checkboxes Run Out The form creates legal clarity. It does not always create clinical clarity. Consider a patient who has accepted cell saver but declined cryoprecipitate. Intraoperatively, they develop a coagulopathy. The surgeons look at you. The anesthesiologist looks at you. The patient is not in a position to revisit their checklist. You are not there to override their documented wishes — you are there to help the team understand what options remain, and what their limits are. In practice, this means knowing your alternatives well enough to deploy them quickly. Can you correct a fibrinogen deficit with a fibrinogen concentrate the patient has accepted? What is the hemostatic ceiling of topical procoagulants like fibrin sealants? Is the surgical team using electrocautery aggressively enough? Is there an interventional radiology option? The transfusion medicine fellow in the bloodless surgery consult is not just a documentarian. You are a consultant in the truest sense — someone whose job is to expand the team’s range of options, not just to manage their expectations. And then there are the cases where the options run out. Where the patient is bleeding and the only thing that would reliably help is a product they have refused. You learn to sit with that. You learn that informed refusal is not a failure of medicine. You learn that the consult you did beforehand — the one where you made sure the patient understood exactly what they were declining, and why, and what the alternatives were — was the most important one. What These Consults Teach You Bloodless surgery consults are a masterclass in what blood products actually do. Because you cannot default to transfusion, you have to explain — to the patient, to the team, and to yourself — exactly what each product is for, what happens physiologically without it, and what can plausibly substitute. You will leave your first few of these consults knowing your coagulation cascade better than you did going in. That is an underappreciated upside. You also learn something about the nature of consent itself. Most informed consent in medicine is procedural: sign here, you understand the risks. Bloodless surgery consent is longitudinal. It happens before the procedure, often well before, and it asks the patient to project themselves into scenarios they cannot fully anticipate. It demands that you, as the consultant, be honest about uncertainty — about what the surgery might require, about which alternatives are genuinely equivalent and which are merely adjacent. Accept or Reject The form implies a binary. Accept. Reject. Medicine is almost never that clean. The most useful thing I can tell a fellow going into their first bloodless surgery consult is this: the form is not the point. The point is the conversation that produces it — the one where you find out what the patient actually believes, what they actually understand, and what they are actually willing to accept when the stakes become real. The checkboxes are documentation. The consult is medicine. And if you leave that room feeling like you understood it completely, you probably missed something.

For roughly twenty years, MOG antibodies were considered noise. Studies kept finding them — in patients with MS, in patients with other demyelinating diseases, in healthy controls. The conclusion the field drew was reasonable: these antibodies probably aren’t doing much. That conclusion was wrong. The antibodies were real. The assay was broken. The protein Myelin oligodendrocyte glycoprotein, MOG, is expressed on the outermost surface of the myelin sheath. Its location matters: it sits on the very outside of oligodendrocytes, fully exposed to the immune system. This makes it a structurally logical target for antibody-mediated attack. Researchers noticed this in the 1980s, when MOG was identified as a potent inducer of experimental autoimmune encephalomyelitis — EAE — the classic animal model used to study multiple sclerosis. MOG-immunized animals developed demyelinating disease. The inference seemed obvious: MOG must be important in human MS, too. That inference was wrong, or at least overstated. But it launched decades of research into MOG antibodies in human demyelinating disease — research that was almost immediately complicated by the tools available to detect them. The assay problem The problem was ELISA. Enzyme-linked immunosorbent assay, ELISA, is a workhorse of antibody detection. It works by coating a solid surface with the antigen of interest — in this case, MOG protein — and then exposing it to patient serum. If antibodies are present, they bind. The trouble is that coating a surface with purified protein requires denaturing it: stripping it out of its native environment, unfolding it, and adhering it flat. What was once a three-dimensional glycoprotein sitting in a lipid bilayer is now a linearized string of amino acids on a plastic plate. For MOG, this matters enormously. The antibodies that are actually relevant in MOGAD recognize a conformational epitope — the specific three-dimensional shape of MOG’s extracellular domain as it exists in a cell membrane. Denatured MOG doesn’t have that shape. So ELISA-based assays were detecting antibodies against linear epitopes, finding them in patients with MS, patients with other demyelinating diseases, and healthy controls. The field grew appropriately skeptical. MOG antibodies looked like noise. The fix: cell-based assays The correction came in 2011 and 2012, with the development of cell-based assays. The approach is straightforward in principle: instead of adhering purified protein to a plate, you transfect cells to express full-length, native MOG on their surface. Patient serum is then incubated with these cells. If MOG-specific IgG is present, it binds to the correctly folded extracellular domain. A fluorescently labeled secondary antibody tags the bound IgG, and flow cytometry — FACS — quantifies the signal. The protein stays where it belongs, embedded in a lipid bilayer, presenting the same conformation the immune system encounters in vivo. The improvement in specificity was dramatic. False positives largely disappeared. A real signal emerged. The door that opened first This methodological breakthrough landed in fertile soil, because the field had already been primed to look. In 2004, Lennon and colleagues published a landmark finding: antibodies against aquaporin-4 — AQP4 — were present in a substantial subset of patients with neuromyelitis optica, or NMO. NMO had long been considered a severe variant of MS. The AQP4 discovery proved otherwise. Here was an antibody-mediated demyelinating disease, clinically and serologically distinct from MS, hiding in the seronegative-MS wastebasket. The discovery raised an obvious question: what was driving disease in the patients who were AQP4-seronegative? A disease takes shape Starting around 2011, groups from Oxford, Munich, and Melbourne began identifying patients — many AQP4-seronegative — with MOG-IgG detected by cell-based assay. Their clinical features were distinctive. Bilateral or simultaneous optic neuritis, sometimes with severe disc edema. Longitudinally extensive transverse myelitis. Acute disseminated encephalomyelitis, particularly in children. Cortical encephalitis. Between attacks, patients often recovered surprisingly well — better than typical MS or AQP4-positive NMOSD. The disease appeared steroid-responsive in ways that also distinguished it from its neighbors on the demyelinating spectrum. This was not MS. It was not AQP4-positive NMOSD. It was something new, or rather, something old that we had finally developed the tools to see. The term MOGAD was formally adopted around 2018 and 2019 to reflect this recognition — a distinct nosological entity with its own clinical phenotype, its own demographic predilections, and its own emerging treatment ladder. In 2023, Banwell and colleagues published international consensus diagnostic criteria in The Lancet Neurology, formalizing what years of cohort data had been building toward. Where apheresis enters Acute attacks are typically treated with high-dose corticosteroids. For patients who don’t respond, intravenous immunoglobulin is a reasonable next step. For steroid-refractory cases, therapeutic plasma exchange enters the picture — mechanistically sensible for an antibody-mediated disease, since removing circulating IgG directly targets what appears to be the primary effector. There is a growing body of case series and retrospective data supporting PLEX in refractory MOGAD attacks, though robust prospective trial data remains limited. The bottom line That last sentence captures something true about MOGAD more broadly. We have a name. We have diagnostic criteria. We have a treatment ladder with reasonable mechanistic logic supporting each rung. What we are still working out — actively, with ongoing trials — is the natural history, the optimal long-term immunosuppression, and the full spectrum of what the disease can look like, particularly in its cortical presentations. MOGAD went from animal model curiosity to false lead to distinct human disease over roughly four decades. The trajectory is a good reminder that the biology usually gets there before we do. Sometimes we’re just waiting for the right assay.

The call comes in sometime around midnight. A patient is febrile and jaundiced, their smear shows ring forms in the red cells, and the parasitemia is sitting at 14%. The infectious disease fellow wants to know if we’ll do an exchange. The answer, for most of us, is yes — and it’s not a difficult yes. High parasitemia, signs of hemolysis, organs starting to wobble. The indication feels obvious. For the better part of forty years, that feeling was the best we had. The first reported case of severe babesiosis treated with RBC exchange was in 1980. Since then, we’ve been doing it based on case reports, small case series, mechanistic logic, and guideline recommendations that were themselves built on case reports, small case series, and mechanistic logic. The data, such as they were, strongly suggested we were doing the right thing. Strongly suggesting and actually demonstrating are different things. Now we have something closer to an actual answer. What We’re Dealing With Babesiosis is a tick-borne illness caused primarily by Babesia microti, an intraerythrocytic parasite — meaning it invades and replicates inside red blood cells. It’s endemic to the northeastern and upper midwestern United States, with incidence increasing steadily in recent years, particularly in New England. The geographic range is expanding, and true incidence may be ten times higher than reported cases. The disease spectrum is wide. Many people clear infection without knowing they had it. Others end up in the ICU. The patients most at risk for severe disease are immunocompromised — asplenic, actively malignant, post-transplant, on immunosuppression — as well as those at the extremes of age. In hospitalized patients, mortality ranges from about 3% to nearly 9% in the general population and can reach 20% in immunocompromised patients. The parasite’s mechanism of harm is hemolysis: infected red cells rupture, releasing hemoglobin into the plasma, triggering endothelial injury, organ dysfunction, and a proinflammatory cytokine cascade that can take on a life of its own. Why Exchange Makes Sense RBC exchange transfusion (ET) is an extracorporeal procedure that removes the patient’s circulating red cells — infected and uninfected alike — while simultaneously replacing them with donor RBCs. The mechanistic case for it in babesiosis is straightforward: fewer infected cells means less hemolysis, less free hemoglobin circulating, and less downstream organ injury. You may also be pulling cytokines out of the circulation, though the contribution of that effect is harder to quantify. The American Society for Apheresis (ASFA) and the Infectious Diseases Society of America (IDSA) have recommended considering ET for patients with high-grade parasitemia (>10%), severe hemolytic anemia, or acute organ injury for years. What neither guideline could do was point to a study with a real control group and say: we know this changes outcomes. The Evidence Problem A 2021 retrospective chart review from Yale (O’Bryan et al., J Clin Apher) was about as rigorous as the pre-existing literature got. Ninety-one patients, single center, 2011–2017. The investigators stratified patients by peak parasitemia — <1%, 1–5%, 5–10%, >10% — and showed that virtually every marker of end-organ dysfunction worsened in a stepwise fashion with increasing parasite burden: hematocrit fell, LDH rose, bilirubin climbed, creatinine drifted up, platelet counts dropped. Nineteen patients received exchange, all with peak parasitemia ≥9% and some degree of organ dysfunction. Parasitemia dropped sharply post-exchange. The study showed what we already believed: high parasite burden is bad, and exchange reduces parasite burden. What it could not show — because patients who received exchange were sicker at baseline than those who didn’t — is whether exchange changed outcomes. The only prior study that even attempted a comparison had twenty-eight total patients. That is the entire controlled evidence base for a procedure we’ve been doing since the Carter administration. Enter STOP-BABESIOSIS In March 2026, Leaf et al. published the STOP-BABESIOSIS study in JAMA Internal Medicine: a multicenter cohort of 3,233 patients hospitalized with babesiosis across 82 sites and 24 medical centers in the northeastern US, spanning 2010 to 2024. Of these, 629 met eligibility criteria for the analysis: parasitemia >10%, or 5–10% with acute organ injury or severe hemolytic anemia. The investigators used a sequential target trial emulation (TTE) framework — worth pausing on, because it matters for how much you trust the result. Target trial emulation is an approach to observational data analysis that mimics the structure of a randomized controlled trial: you specify eligibility criteria, a treatment strategy, a defined start of follow-up, and outcomes, then apply them to real-world data. The sequential version used here enrolls patients on each of the first 7 days of hospitalization, which eliminates a specific bias problem called immortal time bias and allows confounder adjustment at the actual moment of treatment assignment rather than at a fixed earlier time point. Inverse probability of treatment weighting (IPTW) was applied to balance the groups on measured covariates. It’s rigorous methodology, and it’s increasingly the standard when an RCT is impractical. The primary endpoint was a composite of in-hospital death or 30-day readmission. Among the 209 patients treated with ET and the 420 who were not: the composite occurred in 3.6% of ET-treated patients versus 9.8% of those not treated. Adjusted odds ratio: 0.22 (95% CI 0.09–0.51). That’s a nearly fivefold reduction in odds, and it held up across eight sensitivity analyses — adjusted for site, year of admission, LDH, restricted to the first three days, restricted to exchanges using at least 10 units of RBCs. The result didn’t move. What This Changes — and What It Doesn’t The caveats are real, and the authors don’t hide them. Even after IPTW, the ET group had higher median parasitemia and a greater proportion of immunocompromised patients than the control group. Residual confounding in either direction is possible — meaning the benefit of ET could be an underestimate, but it could theoretically also be overestimated. The total number of deaths was small, so most of the composite endpoint is driven by 30-day readmissions rather than mortality. And the study enrolled only patients in the northeastern US, where B. microti is overwhelmingly the dominant species. An RCT is almost certainly never coming. The investigators say so directly, and they’re right: the number of sites required, the enrollment duration, the difficulty of maintaining equipoise, and the inevitability of crossover make a randomized trial effectively impossible. This is the best evidence we are likely to have. And the best evidence we are likely to have is a nearly fivefold reduction in death or readmission among severely ill patients who received exchange. For practice, this means the ASFA/IDSA criteria — parasitemia >10%, or 5–10% with organ injury or severe hemolytic anemia — now have something behind them beyond expert opinion. It also raises questions the study doesn’t answer: which subgroups benefit most? Does exchange help patients who don’t meet the strict eligibility criteria, such as those with milder organ injury? The subgroup analyses showed consistent benefit across age, sex, immunocompromised status, and SOFA score, but the study wasn’t powered for those comparisons. There is a particular kind of clinical discomfort in practicing in an evidence vacuum — ordering a procedure you believe is right, knowing the belief is built on mechanistic logic and small case series rather than data. Most of transfusion medicine lives in that space. Most of medicine does, if you’re being honest about it. The STOP-BABESIOSIS investigators enrolled 3,233 patients across 82 sites over fifteen years to give us an answer for 629 of them who met strict eligibility criteria. That is what it takes to generate evidence in a disease this uncommon. The result is about as close to a definitive answer as we’re going to get. The data support exchange transfusion for severely ill patients with babesiosis. It took forty-six years to say that with numbers behind it. References Leaf DE, et al. (STOP-BABESIOSIS Investigators). Red Blood Cell Exchange Transfusion for Severe Babesiosis. JAMA Intern Med. Published online March 30, 2026. doi:10.1001/jamainternmed.2026.0244 O’Bryan J, Gokhale A, Hendrickson JE, Krause PJ. Parasite burden and red blood cell exchange transfusion for babesiosis. J Clin Apher. 2021;36:127–134. doi:10.1002/jca.21853

The Reaction That Looks Like Everything Else You’re called to the bedside. A patient is forty minutes into a red cell transfusion and their systolic blood pressure has dropped from 130 to 72. They’re not febrile. There’s no rash, no urticaria, no wheezing. The nurses are looking at you. The attending is on the phone. You stop the transfusion, push fluids, and the pressure comes right back up. The patient feels fine. You send your workup — DAT negative, no hemolysis, gram stain unremarkable. Everything comes back normal. What just happened? This is a hypotensive transfusion reaction, and it is one of the more mechanistically interesting reactions we see — precisely because the mechanism has almost nothing to do with the patient’s immune system, and almost everything to do with the tubing between the bag and the vein. The Definition The formal criteria matter here because hypotension is common in sick patients, and not every blood pressure dip during a transfusion is a transfusion reaction. In adults, a hypotensive transfusion reaction is defined as a drop in systolic blood pressure of at least 30 mmHg, with an end systolic pressure at or below 80 mmHg, occurring during or within one hour of cessation of a transfusion. In pediatric patients, the threshold is a greater than 25% drop in systolic blood pressure from baseline. The key distinguishing feature is what’s absent : no fever, no urticaria, no hemolysis, no signs of volume overload, no respiratory distress. The workup is conspicuously clean. This reaction announces itself by exclusion as much as by presentation. The Mechanism: Bradykinin and the Kallikrein-Kinin System To understand why the blood pressure drops, you need to understand what happens to blood as it moves through transfusion tubing and filters — and what that contact triggers at the molecular level. Transfusion tubing and leukoreduction filters present a negatively charged surface to the blood passing through them. That surface contact activates Factor XII, also called Hageman factor, the initiating protease of the contact activation pathway. Once Factor XII is activated, it cleaves prekallikrein into kallikrein. Kallikrein, in turn, cleaves high-molecular-weight kininogen (HMWK) — a plasma protein that serves as a substrate — into bradykinin. Bradykinin is a potent vasodilator. It binds to B2 receptors on vascular endothelium, triggers the release of nitric oxide and prostacyclin, and causes profound smooth muscle relaxation. The result is a rapid drop in systemic vascular resistance and, consequently, in blood pressure. Bradykinin also increases vascular permeability and can cause flushing — which you may or may not see clinically. Under normal circumstances, bradykinin is short-lived. Its half-life is measured in seconds. Angiotensin-converting enzyme, or ACE, is one of the primary enzymes responsible for breaking it down. In a healthy patient with intact ACE activity, bradykinin generated during a transfusion is rapidly degraded before it can accumulate to clinically significant levels. This is where things get interesting. The ACE Inhibitor Connection: A Pharmacologic Vulnerability ACE inhibitors — the lisinopril, enalapril, and ramipril you see on nearly every medication reconciliation in cardiology and nephrology — work by blocking ACE and preventing the conversion of angiotensin I to angiotensin II. This is the intended therapeutic effect. But ACE is a promiscuous enzyme. It doesn’t just process angiotensin. It also degrades bradykinin. In patients on ACE inhibitors, bradykinin clearance is impaired. The same contact activation that generates a tolerable bradykinin load in an untreated patient can generate a clinically significant bradykinin excess in a patient whose primary clearance mechanism is pharmacologically blocked. This is not an allergic reaction. There is no IgE, no mast cell degranulation, no antigen-antibody interaction. It is a pharmacologic vulnerability: the drug does exactly what it was prescribed to do, and in the context of a transfusion, that’s the problem. The incidence of hypotensive transfusion reactions in patients on ACE inhibitors is meaningfully higher than in the general transfused population, though the absolute risk remains low. ACE inhibitor use is the most consistently identified risk factor in the literature. Other proposed risk factors include bedside leukoreduction (as opposed to prestorage leukoreduction), certain filter types, and possibly high infusion rates — though the evidence for these is less robust. It’s worth pausing here to appreciate the elegance of this mechanism, even when you’re standing at the bedside at 2 AM. The same filter we use to reduce febrile reactions and protect against transfusion-associated graft-versus-host disease is generating the vasoactive peptide that’s dropping the blood pressure. The same drug that’s protecting the patient’s kidneys and heart is preventing them from clearing it. Medicine is frequently this kind of double-edged sword. What You Actually Do The good news is that hypotensive transfusion reactions are highly responsive to supportive care. Stop the transfusion, give IV fluids, and in the vast majority of cases the blood pressure recovers fully. More aggressive hemodynamic support is occasionally required but unusual. A few things worth knowing for your management and counseling: Do not rechallenge with the implicated unit.  Once a transfusion has been stopped for a suspected reaction, that unit does not go back up. The patient can receive a different unit if clinically indicated. Hypotensive reactions are stochastic.  This is a reaction generated by a set of conditions during one transfusion — the contact time, the filter surface, the patient’s bradykinin clearance at that moment. It does not necessarily recur. You do not need to permanently modify future blood products based on a single hypotensive reaction. No pre-medications are indicated.  This is a point worth emphasizing because the clinical instinct after a transfusion reaction is to reach for a premedication order. Benadryl and acetaminophen do nothing for bradykinin-mediated hypotension. Prescribing them provides false reassurance without addressing the mechanism — and, as we’ll discuss in a future post, premedications carry their own problems. For future transfusions in a patient who has had a hypotensive reaction, slow the infusion rate, monitor closely, and ensure prestorage-leukoreduced products are being used rather than bedside filtration. Discussing ACE inhibitor timing with the prescribing team is reasonable in patients who have had recurrent reactions, though evidence-based guidance on this is limited. What We Don’t Know Hypotensive transfusion reactions sit in a frustrating space: mechanistically coherent, but epidemiologically murky. The bradykinin story is well-established in the literature, but the clinical predictors of who will react remain poorly characterized. ACE inhibitor use is the best-validated risk factor, but most patients on ACE inhibitors are transfused without incident. There are also open questions about the role of storage time. Bradykinin and other kinins can accumulate in blood products over the storage period, particularly in plasma-rich components. Whether older units carry a higher bradykinin burden at the time of transfusion — and whether that translates to clinical risk — is not firmly established. And then there’s the question of underrecognition. Hypotension is common in hospitalized patients. A modest blood pressure dip during a transfusion in a patient on antihypertensives, diuretics, and vasodilators may never trigger a transfusion reaction workup. How many hypotensive transfusion reactions are quietly absorbed into the background noise of a busy floor? We genuinely don’t know. What we do know is that the mechanism is real, it is pharmacologically explainable, and understanding it makes you a better clinician at the bedside — which is the whole point.

The discourse has two settings, and both are wrong   Pick up any piece about artificial intelligence in medicine and you will find one of two arguments. Either AI is going to revolutionize clinical practice by automating diagnosis and replacing physician judgment, or AI is a dangerous, hallucinating black box that no responsible clinician should touch. Both camps are loud. Both camps are largely arguing past the actual experience of physicians who use these tools.   The thing both arguments have in common is that they imagine AI as an autonomous agent — something that acts independently, makes decisions, and produces outputs you simply accept or reject wholesale. That framing drives the fear and the hype equally. And it doesn't describe how I use AI, or how I think physicians should use it.   There's a better frame. It's called augmented intelligence, and the distinction matters.   What augmented intelligence actually means   Augmented intelligence is not a euphemism for AI with better PR. It describes a specific relationship between the human and the tool: the AI amplifies your thinking, your drafting, your analysis — and you retain intellectual ownership of the output. You are the decision-maker. You direct the work. You evaluate what comes back and correct it when it's wrong. The AI doesn't publish anything. You do.   This is meaningfully different from autonomous AI, which operates independently and generates outputs without ongoing human oversight. The distinction isn't just philosophical — it has real implications for how you build your workflow, how you evaluate output quality, and where accountability sits.   In augmented intelligence, accountability never leaves the physician. That's not a limitation. It's the point.   What this looks like in practice   I use AI tools daily. I use Claude for writing and coding: editing blog posts, structuring arguments, generating diagrams, iterating on prose. I use Gemini for personal assistant tasks — scheduling, reminders, quick lookups. Different tools, different jobs, same underlying principle.   When I'm drafting a post, I bring the idea, the clinical knowledge, the interpretive framework, and the editorial judgment. Claude proposes structure, generates prose, and produces things like SVG diagrams that I couldn't efficiently produce by hand. I read everything. I correct errors — and there are always errors, some subtle. I rewrite passages that don't sound like me or aren't correct. I verify factual claims against primary sources.   The post that goes up is mine. The thinking is mine. The AI accelerated the production of a written artifact that represents my analysis. It did not perform the analysis.   This workflow is only valuable if I maintain that discipline. The moment I start publishing AI output I haven't critically evaluated, I've stopped practicing augmented intelligence and started practicing something more like delegation to a very fluent but unreliable assistant. Those are not the same thing.   The oversight imperative   Anyone who works in laboratory medicine already understands this intuitively, even if they haven't applied the framework to AI.   We do not report analyzer results without understanding what the analyzer did. We run QC. We investigate flags. We understand the assay's limitations, its interference profile, the conditions under which it fails. When a result looks wrong, we don't shrug and report it — we investigate. The instrument is a tool. We are responsible for the result.   AI output requires exactly the same critical scrutiny. The distinctive failure mode of large language models is not that they produce obviously garbled output — it's that they produce fluent, confident, plausible-sounding output that is wrong. A traditional analyzer error usually looks like an error. An AI hallucination often doesn't. It reads like a normal sentence. It cites a study that doesn't exist in the same register as one that does.   This is why oversight isn't optional. It's not a hedge for cautious people. It's the minimum standard for using the tool responsibly. If you're accepting AI output without evaluating it, you're not practicing augmented intelligence. You're practicing something with no quality control, and in medicine, we know exactly how that ends.   The case for engaging now   I understand the instinct to wait. The tools are changing fast. The evidence base for clinical AI is immature. The regulatory landscape is unclear. Sitting it out feels like the prudent move.   But physicians who opt out aren't avoiding risk — they're just outsourcing the learning curve. Someone is going to set the norms for how AI gets used in your institution, your specialty, your practice environment. It will either be clinicians who have hands-on experience with the tools and understand their limitations, or it will be administrators, vendors, and policy-makers who don't see patients.   The physicians who engage critically now — who build workflows with real oversight, who learn where the tools fail, who can articulate what responsible use actually looks like — are the ones who will be positioned to shape those norms. The ones who wait will have AI handed to them later, implemented by people who weren't asking the right questions.   I'd rather be in the first group. I'd rather have colleagues in medicine who are in the first group.   Augmented intelligence, done right, is not about ceding judgment to a machine. It's about using a powerful tool with the same rigor we bring to every other tool in medicine. We validate. We monitor. We maintain accountability. That's not fear-mongering and it's not hype. It's just good practice.

The patient you can't ignore   Picture the consult. Profound neutropenia — ANC in the double digits. Documented fungal infection. Forty-eight hours of broad-spectrum antifungals and still febrile. The primary team is running out of moves.   Someone suggests granulocyte transfusions.   You nod. You place the consult. You mobilize a donor. And somewhere in the back of your mind, a small voice asks: does this actually work?   That voice deserves an answer. The honest answer, unfortunately, is that we're not sure.   Why the idea makes sense   The logic is clean. Neutrophils kill bacteria and fungi. If a patient has no neutrophils — from chemotherapy, from bone marrow failure, from a primary immunodeficiency like chronic granulomatous disease — they can't mount an effective innate immune response. So we give them neutrophils from the outside.   It's the same rationale as any component transfusion: if the patient can't make enough of something critical, and the deficit is causing harm, we try to make up the difference. We do it with red cells. We do it with platelets. Why not neutrophils?   The problem is that logic and evidence are different things. And in transfusion medicine, we have a long history of confusing the two.   The evidence, such as it is   To be clear: we have been trying to answer this question for a long time. There are decades of trials in the granulocyte literature. The field has not been idle. The issue is not a lack of effort — it's that the evidence we've accumulated is genuinely hard to interpret.   Early trials from the 1970s and 1980s showed some promising signals, but they were small, underpowered, and conducted before the era of modern antimicrobial therapy. Patient populations were heterogeneous. Organisms were different. Underlying diseases were different. Comparing across trials is difficult, and drawing conclusions from any individual one is precarious.   More recently, the RING trial — the Resolving Infection in Neutropenia with Granulocytes trial — made a serious attempt to answer the question with a properly designed randomized controlled trial. It was larger and more rigorous than anything that came before. It had a mortality endpoint. It was the study the field needed.   It did not show a survival benefit.   But here's where honest interpretation matters. The RING trial's negative result doesn't necessarily mean granulocytes don't work. The trial faced a fundamental problem: dose. The doses actually delivered to patients were lower than what was considered potentially therapeutic, in part because of the inherent variability in granulocyte collection. Donors were stimulated with G-CSF and dexamethasone, yields varied between donors, and there was no reliable way to guarantee a therapeutic dose on any given day. If you can't reliably deliver the intervention, you can't interpret the result — at least not cleanly.   This is not a minor methodological quibble. It goes to the heart of what the trial can and cannot tell us. RING is the best evidence we have. It is also evidence that came with a major confounder baked in.   The survival curves didn't look dramatically different. The microbiological response data were encouraging in some subgroups and not in others. Secondary endpoints were mixed. You can read the RING trial and come away thinking granulocytes failed a fair test, or you can come away thinking the test itself wasn't quite fair. Both readings are defensible.   We have not arrived at a definitive answer. We may not for a long time.   The amphotericin rule nobody can fully justify   If you've ever been involved in a granulocyte course, you've heard this: separate the granulocytes from the amphotericin. Don't give them at the same time. Space them out — 12 hours if you can.   This is institutional gospel in most centers that do granulocyte transfusions. It's in the AABB Technical Manual. People follow it without question.   Here's what it's actually based on: one paper from 1981 describing pulmonary toxicity in patients who received concurrent granulocytes and amphotericin B. One paper. There were also some in vitro and animal data that suggested a plausible mechanism. That was enough to generate a widespread practice recommendation.   What happened next is instructive. Subsequent clinical studies — multiple of them — tried to confirm this finding and couldn't. The signal didn't replicate. Patients who received granulocytes and amphotericin close together did not consistently have worse pulmonary outcomes than those in whom the infusions were separated.   And yet the practice persisted. The AABB Technical Manual still recommends separation. Centers still coordinate timing. Fellows still field late-night calls about when the liposomal amphotericin was given and whether there's enough of a window.   This is how medical dogma works. A case series raises concern. The concern gets institutionalized. Later evidence fails to confirm it. The institution doesn't notice.   To be clear: there may still be a real interaction. The absence of evidence is not evidence of absence, and the subsequent studies had their own limitations. Separating infusions is low-cost in most clinical situations. But when someone asks you why, the honest answer is: we're not entirely sure, and the original data that started this practice are weaker than the strength of the recommendation would suggest.   A dose we mostly extrapolated   The conventional therapeutic dose target for granulocyte transfusions is at least 1 × 10¹⁰ granulocytes per transfusion. This number comes from dose-response analyses suggesting that below this threshold, there's minimal ANC increment and possibly minimal clinical effect.   There are a few problems with this.   First, collection yields are highly variable. Donors are stimulated with G-CSF and dexamethasone before apheresis, which significantly increases peripheral neutrophil counts and therefore collection efficiency. But even with stimulation, yields vary substantially between donors. Hitting the 1 × 10¹⁰ target is not guaranteed. The RING trial demonstrated this empirically — actual delivered doses in the trial were often below what was intended.   Second, the dose target itself is derived from indirect data. We're using ANC increment as a surrogate for clinical effect, which assumes the transfused neutrophils are functioning effectively after infusion and trafficking to sites of infection. There's evidence they do — labeled granulocytes have been shown to migrate to infection sites — but this is distinct from demonstrating that the dose-response relationship for ANC increment maps neatly onto a dose-response relationship for survival.   Third, we dose by weight (roughly 0.6 × 10⁹ cells/kg as a lower threshold), but we collect a product whose yield is largely determined by donor biology. You can stimulate better. You can select donors with high baseline neutrophil counts. But you can't fully control what you get. The mismatch between what we target and what we deliver is a persistent feature of granulocyte therapy, not a solvable logistics problem.   What to do with all this uncertainty   Granulocyte transfusions are still used. At centers with the infrastructure to collect and process them — which is not everywhere — they remain an option for patients with severe neutropenia and refractory infections, particularly in the setting of primary immunodeficiencies or when marrow recovery is anticipated. The biological rationale is sound. The clinical experience is real, even if it's hard to quantify in controlled trials.   But we should be honest about what we're doing when we order them. We're making a judgment call in the face of genuine uncertainty. We're not executing a protocol backed by level-one evidence. We're doing what makes mechanistic sense for a patient who is out of other options, knowing that our best randomized trial couldn't definitively prove benefit.   That's okay. Clinical medicine involves a lot of this. The problem isn't uncertainty — it's the pretense of certainty. The fellow who confidently states that granulocytes improve survival is wrong. The fellow who confidently states they don't is also wrong. The right answer is that we tried hard to find out, the trial had a fatal flaw in its ability to deliver the intervention reliably, and we're still waiting for better data.   Knowing the limits of the evidence is not a failure of clinical knowledge. It is the clinical knowledge.

There is a particular kind of email that transfusion medicine physicians learn to recognize. It arrives a day or two after an event — a transfusion reaction, a complicated crossmatch, a patient with antibodies nobody quite knew what to do with. The subject line is something like quick question  or following up , and the body begins: I wasn't sure if I was supposed to call you. You weren't sure if you were supposed to call us. This is not a failure of clinical judgment. It is a failure of visibility — and it is one of the most common problems in transfusion medicine, at almost every institution I have ever encountered. Transfusion medicine occupies a strange position in the hospital ecosystem. We are essential infrastructure — the blood bank is running constantly, processing samples, issuing products, catching incompatibilities before they reach patients — but we are largely invisible to the clinicians ordering the blood. We are the electrical grid. You don't think about us until the lights go out. The problem has two distinct roots, and they compound each other. The first is awareness. Many clinicians — including experienced hospitalists, surgeons, and intensivists — do not know that a transfusion medicine consultation service exists, or that there is a physician available to answer questions around the clock. They know there is a blood bank. They may not know there is a board certified physician attached to the blood bank. The second is uncertainty about when to call. Even clinicians who know we exist often hesitate, unsure whether their situation is "bad enough" to warrant a consult. A patient ran a fever during a transfusion — is that ours? The blood bank flagged an antibody — does someone need to talk to me? There is no obvious threshold, no shared mental model of what transfusion medicine is for beyond the most catastrophic scenarios. The result is a gap. Reactions get managed in isolation. Antibody workups proceed without clinical context. Patients occasionally get the right outcome anyway — and occasionally don't. The febrile non-hemolytic transfusion reaction is a useful illustration of both problems at once. FNHTR is common, manageable, and almost never dangerous. Stop the transfusion, give acetaminophen, observe, document. Most hospitalists handle this appropriately without ever calling anyone. That's correct — FNHTR does not require a transfusion medicine consult. But here is where it gets complicated: FNHTR is a diagnosis of exclusion. You can only call it benign after you've ruled out the things that aren't — acute hemolytic reaction, septic transfusion reaction, early TRALI. The fever threshold matters. The hemodynamic picture matters. The timing matters. And a hospitalist who has never been walked through that differential is making a judgment call without a map. Most of the time, the call is right. But "most of the time" is a fragile foundation for patient safety, and the gap between managed correctly in isolation  and should have called us  is narrower than it looks in the moment. I made a resource. It's linked below — a one-page clinical reference for exactly this decision: when to call transfusion medicine, when to monitor, and what to look for in the five reactions that cannot be missed. It is not a substitute for a consult when you are unsure. That's the other thing I want to say plainly: uncertainty is a valid reason to call.  You do not need to have a confirmed hemolytic reaction in front of you to page transfusion medicine. You just need to be unsure. That's enough. We exist. We are available. We want to hear from you before things go wrong — and that is not a high bar. It is just a call.

If you trained anything like I did, you learned transfusion medicine in two separate silos. One bucket: processing. Leukoreduction, irradiation, CMV testing, storage conditions, expiration dates. The other bucket: clinical reactions. Febrile nonhemolytic transfusion reactions, allergic reactions, hypotension, TACO, TRALI. Two completely different lectures, two different shelf exam questions, two different mental filing cabinets. Here's the thing. They're the same story told from different ends. Every decision made during processing has a downstream clinical consequence — sometimes immediate, sometimes delayed, sometimes baked into institutional policy so old that nobody remembers why it exists. Understanding transfusion medicine means collapsing those two silos into one. Let me show you what I mean with four examples. Leukoreduction → FNHTRs and CMV A febrile nonhemolytic transfusion reaction, or FNHTR, is defined as a temperature of at least 38°C with a rise of at least 1°C — or rigors — occurring during or within four hours of the cessation of transfusion. Classically, we're taught that FNHTRs result from cytokine buildup in the unit. That teaching is correct, but it skips the part that makes it interesting. During storage, white blood cells in a blood unit don't just sit there. They die, and as they do, they release cytokines — IL-1, IL-6, TNF-α — that accumulate in the unit over time. By the time that bag of red cells or platelets hangs, it may be carrying a meaningful cytokine payload. Infuse it fast enough, and your patient spikes a fever. Not because of anything intrinsically wrong with the unit. Because you just infused a bag of inflammatory soup. Pre-storage leukoreduction — filtering out the white cells before storage, rather than at the bedside — eliminates the problem at its source. The cytokines never accumulate because the cells that produce them are gone. This is not a trivial distinction: universal leukoreduction significantly reduced FNHTR rates. When we moved from selective to universal leukoreduction in the early 2000s, febrile reactions dropped substantially. But leukoreduction's second accomplishment often gets less airtime, and it deserves more. White blood cells are the primary vector for transfusion-transmitted CMV. CMV is a herpesvirus that establishes latency in leukocytes, and in immunocompetent recipients, transfusion-transmitted CMV is generally clinically silent. In immunocompromised patients — transplant recipients, patients with HIV, premature neonates — it can be devastating. For decades, the solution was CMV seronegative blood: test donors, restrict CMV-negative products to high-risk recipients. The problem is that seronegative status is imperfect. Donors in the window period before seroconversion will test negative and still carry latent virus. Leukoreduction offers a mechanistically cleaner solution: remove the cells that harbor the virus, and you've addressed the problem regardless of serologic status. Current evidence supports leukoreduced blood as equivalent to seronegative blood for CMV-safe transfusion. One processing step. Two major clinical problems addressed. Bedside Filtration → Hypotensive Reactions Here's where it gets interesting. If leukoreduction is so effective, why does it matter when  you filter? The shift from bedside to pre-storage leukoreduction wasn't driven purely by logistics, though the workflow advantages are real. It was also driven by a safety signal. Bedside leukoreduction filters activate the contact pathway of coagulation. That activation generates bradykinin, a potent vasodilator. In most patients, bradykinin is rapidly degraded by angiotensin-converting enzyme, or ACE. But in patients on ACE inhibitors, that degradation pathway is blocked. Bradykinin accumulates, blood pressure drops, and you have a hypotensive transfusion reaction with no fever, no urticaria, no obvious allergic trigger. The processing method determined the patient's risk profile. I'll come back to this one — the bradykinin story is deep enough to deserve its own post — but the principle is the same: a decision made upstream in processing showed up at the bedside. Storage Lesion → Neonatal Practice Red blood cells are not static objects. From the moment they're collected, they change. 2,3-DPG — the molecule that facilitates oxygen offloading from hemoglobin — drops within the first two weeks of storage. Potassium leaks out of the cells and accumulates in the supernatant. The cells become less deformable, less able to squeeze through small capillaries. Microparticles shed from the cell membrane. Collectively, these changes are called the storage lesion. In adult patients with normal physiology, the clinical significance of the storage lesion has been debated extensively. Large randomized trials — ABLE, INFORM, RECESS — have largely failed to show meaningful harm from older blood in most adult populations. The cells aren't great, but adults are fairly forgiving. Neonates are less so. A neonate receiving a large-volume transfusion is exposed to every consequence of the storage lesion in concentrated form. Hyperkalemia from stored red cell supernatant can trigger arrhythmias. Impaired oxygen delivery from 2,3-DPG-depleted cells matters when your patient weighs 700 grams. Deformability matters when you're perfusing vessels measured in microns. This is why neonatal transfusion practice looks so different from adult practice. Fresher units are preferred — the evidence that older units are truly catastrophic for neonates is less definitive than the physiologic concern might suggest, but the caution is reasonable given the stakes. Small-volume aliquots, often washed to reduce potassium load. CMV-safe products. And irradiation — which brings us to the fourth thread. Irradiation → TA-GvHD Transfusion-associated graft-versus-host disease, or TA-GvHD, is rare. It is also, when it occurs, nearly universally fatal — mortality exceeds 90%. That combination makes it one of the most important complications in transfusion medicine, and one of the clearest illustrations of why processing decisions are clinical decisions. Here's the mechanism. Cellular blood products contain viable donor T lymphocytes. In an immunocompetent recipient, those donor T cells are recognized as foreign and eliminated. In an immunocompromised recipient — or in certain other vulnerable populations — they aren't. The donor T cells engraft, proliferate, and begin attacking the host's tissues: skin, liver, gut, bone marrow. The host's own immune system, suppressed or naïve, cannot mount a response. The result is a graft-versus-host syndrome with no good treatment options and very few survivors. The at-risk populations are broader than most people initially assume. Congenital immunodeficiencies, hematologic malignancies, stem cell transplant recipients, and neonates are the obvious ones. Less obvious: patients receiving HLA-matched cellular products, or directed donations from first-degree relatives — situations where the donor and recipient share enough HLA antigens that the recipient's immune system fails to recognize the donor T cells as foreign, even in a host who is otherwise immunocompetent. Irradiation prevents TA-GvHD by delivering a targeted dose of gamma or X-ray radiation to the blood product, rendering donor T lymphocytes incapable of proliferation. The cells are still present — irradiation doesn't remove them — but they can't engraft and they can't divide. The threat is neutralized before the product ever reaches the patient. This is about as direct a processing-to-outcome link as exists in transfusion medicine. A near-universally fatal complication, preventable entirely by a modification applied hours or days before transfusion. The clinician at the bedside never touches it. The outcome depends entirely on whether the right box was checked upstream. The Punchline Processing isn't logistics. It's upstream medicine. The decisions made in processing — when to filter, how to store, what modifications to apply — are clinical decisions, even if the clinicians ordering transfusions rarely think of them that way. When a neonate avoids a hyperkalemic arrest, it's because someone understood the potassium curve on stored blood. When an immunocompromised patient doesn't get CMV, it's because of a filter applied hours before the product ever left the refrigerator. When a patient on lisinopril doesn't bottom out their blood pressure, it's because someone switched from bedside to pre-storage leukoreduction and understood why it mattered. When a post-transplant patient doesn't die of TA-GvHD, it's because a box got checked in a processing facility they'll never set foot in. The two silos were always one subject. We just taught them wrong.