{"id":15643,"date":"2026-07-07T08:00:00","date_gmt":"2026-07-07T13:00:00","guid":{"rendered":"https:\/\/neuroeconomix.com\/?p=15643"},"modified":"2026-07-03T13:51:19","modified_gmt":"2026-07-03T18:51:19","slug":"sesgos-en-los-datos-de-vida-real","status":"publish","type":"post","link":"https:\/\/neuroeconomix.com\/en\/sesgos-en-los-datos-de-vida-real\/","title":{"rendered":"Sesgos en los datos de vida real: cuando m\u00e1s datos no significa mejor evidencia."},"content":{"rendered":"\n
\n

M\u00e1s datos pueden dar m\u00e1s confianza, pero tambi\u00e9n pueden ampliar errores. En evidencia de mundo real (RWE), la pregunta relevante es si el dato representa bien la realidad cl\u00ednica, administrativa y social que se quiere analizar.<\/em><\/em><\/em><\/em><\/em><\/em><\/em><\/em><\/em><\/em><\/strong><\/p>\n<\/blockquote>\n\n\n\n

\n\n\n\n

<\/p>\n\n\n\n

Los sesgos en los datos de vida real que vemos en el art\u00edculo \u201ccuando m\u00e1s datos no significa mejor evidencia\u201d pueden distorsionar decisiones cl\u00ednicas, financieras y regulatorias. La evidencia de mundo real promete respuestas \u00fatiles, pero solo si las historias cl\u00ednicas, las bases administrativas, las aplicaciones y los dispositivos vestibles se validan con rigor.<\/p>\n\n\n\n

Evidencia de mundo real: dato que responde la pregunta<\/h5>\n\n\n\n

La evidencia de mundo real (RWE, por su sigla en ingl\u00e9s) surge del an\u00e1lisis de datos de vida real: historias cl\u00ednicas, bases administrativas, aplicaciones m\u00f3viles y dispositivos vestibles. La Administraci\u00f3n de Alimentos y Medicamentos de Estados Unidos la define como evidencia cl\u00ednica derivada del an\u00e1lisis de estos datos para evaluar el uso, los beneficios o los riesgos de un producto m\u00e9dico (1). Esa amplitud de fuentes no elimina el sesgo; a veces lo distribuye mejor y lo vuelve menos visible.<\/p>\n\n\n\n

Para una Entidad Promotora de Salud (EPS), donde exista esa figura, o para un pagador regional, esto importa al autorizar los medicamentos de alto costo. Una base grande puede mostrar menor hospitalizaci\u00f3n con un tratamiento, pero quiz\u00e1 esos pacientes asistieron a m\u00e1s controles o ten\u00edan mejor adherencia. Este es el primer recordatorio de los sesgos en los datos de vida real cuando m\u00e1s datos no significa mejor evidencia: el volumen parece robusto, aunque la comparaci\u00f3n no sea justa.<\/p>\n\n\n\n

Digitalizaci\u00f3n regional con leyes e interoperabilidad desiguales<\/h5>\n\n\n\n

En Latinoam\u00e9rica, la agenda de salud digital avanza con velocidades distintas. El Banco Interamericano de Desarrollo report\u00f3 dos brechas: solo 11 pa\u00edses ten\u00edan legislaci\u00f3n sobre sistemas de historia cl\u00ednica electr\u00f3nica; adem\u00e1s, 14 de 26 pa\u00edses encuestados contaban con una estrategia de salud digital (2). Por su parte, la Organizaci\u00f3n Panamericana de la Salud se\u00f1ala que los sistemas de informaci\u00f3n deben fortalecer la gobernanza, la infraestructura tecnol\u00f3gica, los est\u00e1ndares, la interoperabilidad, la privacidad y el procesamiento de datos (3). Esta combinaci\u00f3n revela una tensi\u00f3n: hay m\u00e1s datos, pero no siempre hay datos comparables.<\/p>\n\n\n\n

Un director de cl\u00ednica puede verlo en un tablero mensual. Una Instituci\u00f3n Prestadora de Salud codifica la diabetes con detalle. Otra IPS la deja en texto libre y una tercera solo la registra cuando afecta la facturaci\u00f3n. Por eso, si esas fuentes alimentan un an\u00e1lisis regional, la diferencia puede parecer epidemiol\u00f3gica cuando en realidad es operativa. La interoperabilidad significa que diagn\u00f3sticos, procedimientos, resultados y fechas conserven el mismo significado al moverse entre instituciones.<\/p>\n\n\n\n

Selecci\u00f3n, subregistro y diferencias entre IPS cambian el resultado<\/h5>\n\n\n\n

El sesgo de selecci\u00f3n aparece cuando la base incluye mejor a ciertos pacientes que a otros. La literatura sobre calidad de datos en historias cl\u00ednicas destaca que la reutilizaci\u00f3n secundaria exige revisar dimensiones como completitud, consistencia y plausibilidad, no solo disponibilidad t\u00e9cnica (4). Adem\u00e1s, los estudios con historias cl\u00ednicas y bases administrativas pueden conservar sesgos que no desaparecen al aumentar el tama\u00f1o muestral; un mill\u00f3n de registros mal seleccionados puede estimar con precisi\u00f3n una respuesta equivocada (5).<\/p>\n\n\n\n

Un ejemplo cotidiano ocurre en oncolog\u00eda. Si una red privada registra biomarcadores con m\u00e1s frecuencia que una red p\u00fablica, un an\u00e1lisis de supervivencia podr\u00eda concluir que los pacientes \u201ccon biomarcador\u201d evolucionan mejor. Sin embargo, ese resultado puede reflejar acceso, oportunidad diagn\u00f3stica y capacidad de registro, no solo biolog\u00eda tumoral. Estos sesgos en los datos de vida real obligan a separar el efecto cl\u00ednico del efecto institucional.<\/p>\n\n\n\n

El subregistro opera de manera menos visible. Las bases administrativas suelen capturar bien lo que se cobra, pero pueden omitir la severidad, los s\u00edntomas, los resultados reportados por el paciente o los eventos manejados fuera de la red. La declaraci\u00f3n RECORD (REporting of studies Conducted using Observational Routinely-collected health Data) pide reportar y validar los c\u00f3digos o algoritmos usados para definir poblaciones y desenlaces (6). Si una urgencia no registra la escala de dolor o el motivo de una readmisi\u00f3n, el an\u00e1lisis posterior pierde contexto.<\/p>\n\n\n\n

Aplicaciones y dispositivos vestibles: se\u00f1ales \u00fatiles con validaci\u00f3n limitada<\/h5>\n\n\n\n

Las aplicaciones m\u00f3viles, o apps, y los dispositivos vestibles, llamados wearables, ampl\u00edan la observaci\u00f3n fuera del consultorio. Pueden registrar pasos, sue\u00f1o, frecuencia cardiaca, glucosa o s\u00edntomas reportados por el paciente. Sin embargo, una se\u00f1al continua no equivale por s\u00ed sola a un desenlace cl\u00ednico v\u00e1lido. La gu\u00eda de la Administraci\u00f3n de Alimentos y Medicamentos sobre adquisici\u00f3n remota de datos recomienda evaluar si estas tecnolog\u00edas son apropiadas y est\u00e1n validadas antes de usarlas como fuente (7).<\/p>\n\n\n\n

Piense en un paciente con falla cardiaca que usa un reloj inteligente. Si el dispositivo deja de medir cuando se descarga, si el paciente lo retira al dormir o si la aplicaci\u00f3n solo la usan personas con tel\u00e9fonos recientes, los datos incompletos pueden concentrarse en quienes tienen m\u00e1s riesgo. As\u00ed, una app puede mostrar buena adherencia porque registra mejor a los pacientes m\u00e1s conectados. Para una aseguradora o una red hospitalaria, esa diferencia puede sesgar un programa de gesti\u00f3n de riesgo.<\/p>\n\n\n\n

Convertir volumen en evidencia exige validaci\u00f3n deliberada<\/h5>\n\n\n\n

La Agencia Europea de Medicamentos cuenta con un marco de calidad de datos para decisiones regulatorias, aplicable tambi\u00e9n a la evaluaci\u00f3n de datos de vida real en medicamentos (8). En paralelo, la plantilla Structured Template and Reporting Tool for Real-World Evidence (STaRT-RWE) propone planear y reportar los estudios de RWE con mayor transparencia (9). Estas referencias no sustituyen la regulaci\u00f3n local, pero ofrecen un criterio pr\u00e1ctico para Latinoam\u00e9rica: definir primero la decisi\u00f3n y despu\u00e9s evaluar si la fuente sirve.<\/p>\n\n\n\n

Para una IPS, un pagador o una autoridad sanitaria, conviene empezar con tres preguntas:<\/p>\n\n\n\n

\u2022 \u00bfLa poblaci\u00f3n representa a quienes deciden?<\/p>\n\n\n\n

\u2022 \u00bfSe validaron los diagn\u00f3sticos y desenlaces contra una fuente cl\u00ednica confiable?<\/p>\n\n\n\n

\u2022 \u00bfSe describieron los datos faltantes por instituci\u00f3n, edad, sexo y territorio?<\/p>\n\n\n\n

Ese ejercicio no convierte autom\u00e1ticamente los datos en evidencia causal. Sin embargo, permite identificar los sesgos en los datos de vida real y evita que un tablero elegante o una base masiva se confundan con una conclusi\u00f3n s\u00f3lida. La frase \u201csesgos en los datos de vida real cuando m\u00e1s datos no significa mejor evidencia\u201d deja una advertencia concreta para Latinoam\u00e9rica: cada dato necesita origen claro, significado compartido y l\u00edmite expl\u00edcito.<\/p>\n\n\n\n

Pr\u00f3ximo paso<\/h5>\n\n\n\n

Si est\u00e1s en la industria farmac\u00e9utica y quieres usar la IA para demostrar el valor de tu medicamento al pagador cont\u00e1ctanos.<\/p>\n\n\n\n

Si quieres m\u00e1s informaci\u00f3n sobre QUANTUS<\/strong>, escr\u00edbenos aqu\u00ed: https:\/\/www.getquantus.ai\/<\/a><\/p>\n\n\n\n

\n\n\n\n

<\/p>\n\n\n\n

Referencias<\/mark><\/strong><\/p>\n\n\n\n

1. U.S. Food and Drug Administration. Real-world evidence. Silver Spring: FDA; [consultado 23 Jun 2026]. Disponible en: https:\/\/www.fda.gov\/science-research\/science-and-research-special-topics\/real-world-evidence<\/a><\/p>\n\n\n\n

2. Inter-American Development Bank. IDB report: Digital transformation of healthcare systems saves lives, lowers costs. Washington: IDB; [consultado 23 Jun 2026]. Disponible en: https:\/\/www.iadb.org\/en\/news\/idb-report-digital-transformation-healthcare-systems-saves-lives-lowers-costs<\/a><\/p>\n\n\n\n

3. Pan American Health Organization. Health information systems. Washington: PAHO; [consultado 23 Jun 2026]. Disponible en: https:\/\/www.paho.org\/en\/topics\/health-information-systems<\/a><\/p>\n\n\n\n

4. Kahn MG, Callahan TJ, Barnard J, Bauck AE, Brown J, Davidson BN, et al. A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. eGEMs. 2016;4(1):1244. Disponible en: https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC5051581\/<\/a><\/p>\n\n\n\n

5. Kundu R, Shi X, Morrison J, Barrett J, Mukherjee B. A framework for understanding selection bias in real-world healthcare data. J R Stat Soc Ser A Stat Soc. 2024;187(3):606-635. Disponible en: https:\/\/pubmed.ncbi.nlm.nih.gov\/39281782\/<\/a><\/p>\n\n\n\n

6. Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. Disponible en: https:\/\/journals.plos.org\/plosmedicine\/article?id=10.1371\/journal.pmed.1001885<\/a><\/p>\n\n\n\n

7. U.S. Food and Drug Administration. Digital health technologies for remote data acquisition in clinical investigations: guidance for industry. Silver Spring: FDA; 2023 [consultado 23 Jun 2026]. Disponible en: https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/digital-health-technologies-remote-data-acquisition-clinical-investigations<\/a><\/p>\n\n\n\n

8. European Medicines Agency. Data quality framework for medicines regulation. Amsterdam: EMA; [consultado 23 Jun 2026]. Disponible en: https:\/\/www.ema.europa.eu\/en\/about-us\/how-we-work\/data-regulation-big-data-other-sources\/data-quality-framework-medicines-regulation<\/a><\/p>\n\n\n\n

9. Wang SV, Schneeweiss S, Berger ML, Brown J, de Vries F, Douglas I, et al. STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies. BMJ. 2021;372:m4856. Disponible en: https:\/\/pubmed.ncbi.nlm.nih.gov\/33436424\/<\/a><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

M\u00e1s datos pueden dar m\u00e1s confianza, pero tambi\u00e9n pueden ampliar errores. En evidencia de mundo real (RWE), la pregunta relevante es si el dato representa bien la realidad cl\u00ednica, administrativa y social que se quiere 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