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Revelando lo invisible en España: Cómo la tecnología UV-C está impactando al mercado europeo

Puntos críticos: La pandemia validó la tecnología UV-C para el mundo médico, y ahora se están encontrando usos en otras industrias. El mercado español tiene varios sectores donde los rayos ultravioletas tipo C puedes ser útiles. El ecosistema de tecnologías sanitarias será critico para la salud de gente en nuestros ambientes públicos. El último año…

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Revelando lo invisible en España: Cómo la tecnología UV-C está impactando al mercado europeo

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  • La pandemia validó la tecnología UV-C para el mundo médico, y ahora se están encontrando usos en otras industrias.
  • El mercado español tiene varios sectores donde los rayos ultravioletas tipo C puedes ser útiles.
  • El ecosistema de tecnologías sanitarias será critico para la salud de gente en nuestros ambientes públicos.

El último año y medio de disrupciones sanitarias ha revelado la importancia de mejores prácticas para mantener la salud de la gente en lugares públicos y de alto tráfico. En el primer episodio hispanohablante de Making the Invisible, Visible, un podcast de Intellego Technologies exploramos los beneficios de UV-C en este contexto. Vamos a explorar los casos de uso para rayos ultravioletas tipo C en las tecnologías biomédicas, pero no solo explorando como funciona la tecnología; también daremos puntos de vista en como maximizar esta tecnología en un ecosistema de soluciones innovadoras.

Nuestras invitadas, Patricia Pregel y Verónica Vidal, explican cómo esta tecnología ha avanzada en años recientes, y porque en la región europea, específicamente en España, se puede encontrar usos en varias industrias. Darémos respuestas a preguntas importantes, como…

  1. ¿Cómo funciona como una solución biomédica, y cómo se compara a otros rayos ultravioletas?
  2. ¿Cómo funcionan entro del ecosistema de tecnologías de saneamiento? ¿Qué reemplazan y qué complementan?
  3. ¿En cuales sectores españoles hay la mayoría de la oportunidades para usar el ecosistema de productos UV-C, y por qué?

Más información sobre nuestras invitadas:

  • Patricia Pregel es la directora de ventas de Intellego Technologies para el sur de Europa, LATAM y África.
  • Verónica Vidal está a cargo de Directora de Biodosimetría e Innovación para Fotoglass, una compañía creando aplicaciones ópticas para el sector industrial y biomédico, validando a la eficacia de tecnología crítica a estos sectores.

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Smart ICU and ambient AI cut errors when they feed data and notes into the EMR

Smart ICU and ambient AI cut errors when they feed data and notes into the EMR

Two HIMSS26 APAC case studies point to the same operational lesson: hospitals are getting measurable gains from “smart ICU” device integration and ambient AI documentation only when those tools are tightly integrated into core clinical workflows. Pondok Indah Hospital Group in Indonesia reported reductions of up to 70% in ICU administrative errors and 40% in adverse drug reactions after integrating smart devices, according to Healthcare IT News. Sir H.N. Reliance Foundation Hospital in India reported ambient AI is now used for nearly 90% of progress notes and shift handovers across five live use cases on a single EMR-integrated platform, also reported by Healthcare IT News. New JAMA Network cardiovascular research adds a parallel signal on the clinical side, with AI-enabled acquisition and interpretation approaches moving into screening and triage workflows, which raises procurement questions about validation, interoperability, and change management at the bedside.

  • 01A useful benchmark is emerging for documentation automation: “nearly 90% of progress notes and shift handovers” on ambient AI when it is deployed as one EMR-integrated platform, not a set of point tools (Healthcare IT News).
  • 02The measurable ROI in ‘smart ICU’ programs shows up where operators feel pain: fewer administrative errors and medication-related events, not in abstract “digitization” metrics (Healthcare IT News reported up to 70% and 40% reductions, respectively).
  • 03For hospitals with multiple device vendors and fragmented documentation workflows, integration work, interfaces, identity, order context, and governance, is likely to consume more effort than model selection, so contracts and implementation plans should price integration explicitly.

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Gartner says AI budgets are growing faster than the rules to control them

Gartner says AI budgets are growing faster than the rules to control them

Gartner’s late-August 2026 research points to a familiar operational pattern in enterprise AI: budgets are rising faster than the controls meant to keep costs and risk predictable. In a Aug. 26 press release, Gartner said AI spending by customer service leaders surged 38% even as overall service and support budgets rose 2%. Earlier, at Gartner’s March 2026 Data & Analytics Summit, Gartner analysts said only 44% of organizations had adopted financial guardrails or AI FinOps practices, a gap that becomes more painful as AI workloads scale. The practical takeaway for CIOs, customer service operations leaders, and data and analytics teams is to treat AI governance, cost attribution, and human escalation paths as procurement requirements, not after-the-fact fixes.

  • 01A useful benchmark for planning: Gartner pegs AI spend growth in customer service at 38% versus 2% budget growth overall, a mismatch that forces reallocation and harder ROI proof.
  • 02Only 44% of organizations have adopted AI FinOps-style guardrails, according to Gartner. If AI is moving into production, chargeback and consumption limits need to be designed into the rollout.
  • 03Gartner also forecasts spending on securing AI will hit $4.8 billion in 2027, signaling that AI security is becoming a standalone budget line rather than a feature bundled into existing platforms.

Sep 1, 2026

How Targeted Patient Education Improves Outcomes - Stephen Page, SmarterHealth.AI

How Targeted Patient Education Improves Outcomes - Stephen Page, SmarterHealth.AI

Targeted patient education powered by AI can reduce preventable readmissions and improve health equity, but healthcare organizations must prioritize clinical oversight, data security, and ethical governance when implementing these solutions.

  • 01Preventable readmissions occur when patients misunderstand medications, miss symptom recognition, or lack clarity on follow-up instructions, creating clinical, operational and financial burdens for healthcare systems
  • 02AI-delivered patient education must meet three criteria: solve a meaningful clinical or financial problem, integrate naturally into care workflows, and avoid adding burden to patients or clinicians
  • 03Healthcare leaders evaluating AI tools should prioritize solutions that improve patient understanding, support clinicians, reduce avoidable utilization, protect data security, and demonstrate measurable clinical or financial results

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