#decision support systems

[divakarmrfr-healthcare]

Clinical Decision Support Systems (CDSS) Market Trends and Insights by Component (Software, Services, Hardware), Product (Integrated CDSS, Standalone...

[mostlysignssomeportents]

AI can’t do your job

I'm on a 20+ city book tour for my new novel PICKS AND SHOVELS. Catch me in SAN DIEGO at MYSTERIOUS GALAXY on Mar 24, and in CHICAGO with PETER SAGAL on Apr 2. More tour dates here.

AI can't do your job, but an AI salesman (Elon Musk) can convince your boss (the USA) to fire you and replace you (a federal worker) with a chatbot that can't do your job:

https://www.pcmag.com/news/amid-job-cuts-doge-accelerates-rollout-of-ai-tool-to-automate-government

If you pay attention to the hype, you'd think that all the action on "AI" (an incoherent grab-bag of only marginally related technologies) was in generating text and images. Man, is that ever wrong. The AI hype machine could put every commercial illustrator alive on the breadline and the savings wouldn't pay the kombucha budget for the million-dollar-a-year techies who oversaw Dall-E's training run. The commercial market for automated email summaries is likewise infinitesimal.

The fact that CEOs overestimate the size of this market is easy to understand, since "CEO" is the most laptop job of all laptop jobs. Having a chatbot summarize the boss's email is the 2025 equivalent of the 2000s gag about the boss whose secretary printed out the boss's email and put it in his in-tray so he could go over it with a red pen and then dictate his reply.

The smart AI money is long on "decision support," whereby a statistical inference engine suggests to a human being what decision they should make. There's bots that are supposed to diagnose tumors, bots that are supposed to make neutral bail and parole decisions, bots that are supposed to evaluate student essays, resumes and loan applications.

The narrative around these bots is that they are there to help humans. In this story, the hospital buys a radiology bot that offers a second opinion to the human radiologist. If they disagree, the human radiologist takes another look. In this tale, AI is a way for hospitals to make fewer mistakes by spending more money. An AI assisted radiologist is less productive (because they re-run some x-rays to resolve disagreements with the bot) but more accurate.

In automation theory jargon, this radiologist is a "centaur" – a human head grafted onto the tireless, ever-vigilant body of a robot

Of course, no one who invests in an AI company expects this to happen. Instead, they want reverse-centaurs: a human who acts as an assistant to a robot. The real pitch to hospital is, "Fire all but one of your radiologists and then put that poor bastard to work reviewing the judgments our robot makes at machine scale."

No one seriously thinks that the reverse-centaur radiologist will be able to maintain perfect vigilance over long shifts of supervising automated process that rarely go wrong, but when they do, the error must be caught:

https://pluralistic.net/2024/04/01/human-in-the-loop/#monkey-in-the-middle

The role of this "human in the loop" isn't to prevent errors. That human's is there to be blamed for errors:

https://pluralistic.net/2024/10/30/a-neck-in-a-noose/#is-also-a-human-in-the-loop

The human is there to be a "moral crumple zone":

https://estsjournal.org/index.php/ests/article/view/260

The human is there to be an "accountability sink":

https://profilebooks.com/work/the-unaccountability-machine/

But they're not there to be radiologists.

This is bad enough when we're talking about radiology, but it's even worse in government contexts, where the bots are deciding who gets Medicare, who gets food stamps, who gets VA benefits, who gets a visa, who gets indicted, who gets bail, and who gets parole.

That's because statistical inference is intrinsically conservative: an AI predicts the future by looking at its data about the past, and when that prediction is also an automated decision, fed to a Chaplinesque reverse-centaur trying to keep pace with a torrent of machine judgments, the prediction becomes a directive, and thus a self-fulfilling prophecy:

https://pluralistic.net/2023/03/09/autocomplete-worshippers/#the-real-ai-was-the-corporations-that-we-fought-along-the-way

AIs want the future to be like the past, and AIs make the future like the past. If the training data is full of human bias, then the predictions will also be full of human bias, and then the outcomes will be full of human bias, and when those outcomes are copraphagically fed back into the training data, you get new, highly concentrated human/machine bias:

https://pluralistic.net/2024/03/14/inhuman-centipede/#enshittibottification

By firing skilled human workers and replacing them with spicy autocomplete, Musk is assuming his final form as both the kind of boss who can be conned into replacing you with a defective chatbot and as the fast-talking sales rep who cons your boss. Musk is transforming key government functions into high-speed error-generating machines whose human minders are only the payroll to take the fall for the coming tsunami of robot fuckups.

This is the equivalent to filling the American government's walls with asbestos, turning agencies into hazmat zones that we can't touch without causing thousands to sicken and die:

https://pluralistic.net/2021/08/19/failure-cascades/#dirty-data

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2025/03/18/asbestos-in-the-walls/#government-by-spicy-autocomplete

Image: Krd (modified) https://commons.wikimedia.org/wiki/File:DASA_01.jpg

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

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[marketresearchhealthcarereports]

Clinical Decision Support Systems (CDSS) Market Trends and Insights by Component (Software, Services, Hardware), Product (Integrated CDSS, Standalone...

[ss-1990]

Lab Analyzers Interfacing: Bridging the Gap Between Data and Action

In the dynamic landscape of laboratory operations, the seamless integration of Lab Analyzers Interfacing plays a pivotal role in transforming raw data into actionable insights. By bridging the gap between data generation and actionable outcomes, these interfaces facilitate efficient decision-making and enhance overall laboratory performance.

Seamless Data Integration: Seamless data integration is the cornerstone of effective lab analyzer interfacing. It enables disparate laboratory instruments and systems to communicate seamlessly, ensuring that data flows seamlessly throughout the laboratory ecosystem. With seamless data integration, laboratories can consolidate data from various sources, such as analyzers, LIS (Laboratory Information Systems), and EMR (Electronic Medical Records), enabling comprehensive analysis and reporting.

Real-time Analytics: Leveraging real-time analytics, laboratories can gain immediate insights into test results and performance metrics. By analyzing data as it is generated, laboratory professionals can identify trends, anomalies, and potential issues in real time, enabling proactive decision-making and intervention. Real-time analytics empower laboratories to optimize workflows, prioritize tasks, and deliver timely results to healthcare providers and patients.

Workflow Automation: Workflow automation streamlines laboratory processes by automating routine tasks and optimizing resource utilization. Through workflow automation, tasks such as sample handling, testing, and result reporting can be automated, reducing manual errors and accelerating turnaround times. By automating repetitive tasks, laboratories can enhance efficiency, improve throughput, and allocate resources more effectively.

Quality Assurance Measures: Maintaining quality assurance is paramount in laboratory operations to ensure the accuracy and reliability of test results. Lab analyzer interfacing enables the implementation of robust quality assurance measures, including instrument calibration, proficiency testing, and result validation. By enforcing stringent quality control protocols, laboratories can uphold the highest standards of accuracy and reliability in diagnostic testing.

Decision Support Systems: Integrated decision support systems empower laboratory professionals with actionable insights and recommendations based on data analysis. These systems leverage advanced algorithms and machine learning techniques to assist in result interpretation, diagnosis, and treatment planning. By providing evidence-based guidance, decision support systems enable laboratories to deliver more informed and personalized care to patients.

In conclusion, Lab Analyzers Interfacing plays a crucial role in bridging the gap between data generation and actionable outcomes in laboratory settings. Through seamless data integration, real-time analytics, workflow automation, quality assurance measures, and decision support systems, laboratories can enhance efficiency, accuracy, and overall performance, ultimately improving patient care and outcomes.

[techniktagebuch]

1984

SYSTEM W: So begann Business Intelligence für mich

Von 1984 bis 1987 arbeite ich in Köln bei der Comshare AG. Das Unternehmen kommt ursprünglich aus dem Timesharing-Business, d. h. es lebt vom Verkauf von Rechenzeit. Das Geschäft funktioniert nicht mehr so gut und bei der Suche nach einem neuen Geschäftsfeld entscheidet sich das Management in der Zentrale in Ann Arbor, Michigan, für eine Produktpalette rund um das Thema Management-Unterstützung (Decision Support Systems, DSS). Zu der Zeit haben Manager in der Regel (noch) keinen Rechner auf ihrem Schreibtisch, sondern lassen sich Auswertungen auf Papier liefern, manchmal auf großen Stapeln Papier. Die neue Software ‚System W‘ (von der Marke ‚Wizard‘ ist man schnell wieder abgekommen, weil ein französischer Toilettenreiniger so heißt) soll auf Terminal-Bildschirmen laufen und erlauben, die unterschiedlichsten Datenquellen über SQL-Kommandos miteinander zu verbinden und neue Auswertungen zu erlauben.

IBM-PCs verbreiten sich aber in immer mehr Unternehmen und Comshare bietet für sein eigentlich mainframebasiertes ‚System W‘ eine PC-Komponente an – grafische Auswertungen auf dem PC-Bildschirm, der zu der Zeit eigentlich noch von Zeichendarstellungen von 80 Zeichen in 40 Zeilen dominiert wird – die grafischen Qualitäten von Computermonitoren sind noch eher rudimentär. Die Software imponiert Kunden und Kollegen durch einfach zu erzeugende Kuchen- und Säulengrafiken, hat aber den Fehler, dass sie exakt die vorhandenen 640 KB Hauptspeicher des MS-DOS-Standards ausnutzt. Das bedeutet für den deutschen Markt, dass der Tastaturtreiber für die deutsche Tastatur (ca. 5 KB groß) nicht mehr geladen werden kann – damit wird der PC für deutsche Benutzer unbrauchbar, denn Umlaute fehlen ganz und die Sonderzeichen liegen alle auf anderen Tasten als gewohnt. Die Lösung: Mit jeder PC-Lizenz liefern wir extra für diesen Zweck bestellte Tastaturen im US-Layout aus – es gibt zwar immer noch keine Umlaute, aber wenigstens sieht man die Sonderzeichen wie Bindestriche etc. im Klartext auf der Tastatur.

Möglicherweise habe ich einige Zwischenstufen ausgelassen, aber DSS (Decision Support Systems) mutiert über die Zeit zu EIS (Executive Information Systems), dann zu CPM (Corporate Performance Management) und später zu BI (Business Intelligence). BI ist heute noch (zum Aufschreibezeitpunkt 2022) ein Standard in Unternehmen – immer noch mit den gleichen Versprechungen wie damals: Laien ohne Programmierkenntnisse können sich ihre eigenen Berichte zusammenbauen.

Die Herausforderung ist – damals wie heute: Wie bekommt man die Komplexität beherrscht? Und damals wie heute ist die Antwort auf dieses Problem nicht leicht. Sowohl Datenstrukturen als auch Ergebnisse wollen in einfacher Weise dargestellt werden. Wer sich aber mit Excel auskennt und Pivot-Tabellen beherrscht, ist auf dem besten Weg zum BI-Anwender.

(Rainer Glaap)

[inferscience]

A clinical decision support system (CDSS) is a health information technology system that is designed to offers information to clinicians and primary care providers to improve the quality of the care their patients receive. In this infographic, you can read about the characteristics of clinical decision support systems.

[stephenroger1001-blog]

Accely' decision support systems and product evaluation strategy including leading life sciences company optimize discovery and pre-clinical processes and strategy.