I Congresso Virtual de Medicina Geral e Familiar (MGF)

Portugal lança I Congresso Virtual de Medicina Geral e Familiar Cerca de 1300 médicos de 57 países vão ligar-se “em rede” no I Congresso Virtual de Medicina Geral e Familiar (MGF), que tem inicio esta terça-feira. O encontro, organizado em Portugal, visa a troca de experiências quer do ponto de vista clínico, quer cultural.   ( 10:39 / 01 de Abril 08 )

Cerca de 1300 médicos de 57 países vão ligar-se “em rede” no I Congresso Virtual de Medicina Geral e Familiar (MGF), que tem inicio esta terça-feira e pretende fomentar a troca de experiências entre um número maior de profissionais de Saúde. O presidente da Associação Portuguesa dos Médicos de Clínica Geral (APMCG) explicou à TSF que o «congresso ocorre na Internet», evitando a deslocação dos clínicos, e conta com 1300 «médicos de família de todo o mundo», 800 dos quais portugueses e 240 brasileiros. O encontro «vai permitir trocar experiências quer do ponto de vista clínico, quer do ponto de vista cultural», visando o desenvolvimento da especialidade, adiantou Luís Pisco, que preside ao congresso. A iniciativa, organizada em Portugal, conta com o apoio da Organização Mundial de Médicos de Família. A cerimónia de lançamento do congresso, esta terça-feira pelas 17:00, pode ser visualizada na Internet pelo público em geral, através da página electrónica do I Congresso Virtual de Medicina Geral e Familiar.

http://www.congressovirtualmgf.com/

It's the Network, Stupid: Why Everything in Medicine Is Connected

The PLoS Medicine Editors

 

Citation: The PLoS Medicine Editors (2008) It’s the Network, Stupid: Why Everything in Medicine Is Connected. PLoS Med 5(3): e71 doi:10.1371/journal.pmed.0050071

Published: March 25, 2008

Copyright: © 2008 The PLoS Medicine Editors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The PLoS Medicine Editors are Virginia Barbour, Jocalyn Clark, Larry Peiperl, Emma Veitch, and Gavin Yamey.

E-mail: medicine_editors@plos.org

---

One need look no further than Facebook to appreciate the significance and power of social networking. (Even PLoS has its own thriving Facebook community, which you can join at http://www.facebook.com/group.php?gid=2401713690.) But social networking is about more than just friends reunited; it’s a framework for understanding even the most basic of biological processes. Two papers in this month’s PLoS Medicine illustrate the insight that network theory brings to basic science, and the valuable interdisciplinarity that social network analysis can inspire.

Once the domain of social scientists—who have used social network analysis to study such diverse phenomena as kinship ties, organizational behavior, rumor spreading, and global air traffic—network theory has now entered the purview of health scientists. Network theory is concerned with mapping the links between entities, and social network analysis is the application of that theory to the social sciences. Searching for more social and environmental explanations for the obesity epidemic in America, for example, Christakis and Fowler [1] showed that obesity can spread from person to person, and that this spread depends on the nature of social ties: a person’s chance of becoming obese increased by 171% if he or she had a mutual friend who had become obese (even if they lived far away). Their risk increased by 40% if it was their sibling or spouse who became obese. Christakis and Fowler concluded that the social network is a crucial component—perhaps more so than genetics—in explaining obesity, a problem normally thought of as solely biological and behavioral.

Similarly, a major advance in stemming an outbreak of early syphilis in San Francisco was accomplished through understanding social networks. Klausner and colleagues found that the outbreak was tied to a network of sexual contacts who were meeting through Internet chat rooms [2]. The public health department was then able to initiate an electronic awareness and partner notification campaign using the same Web-based sexual network; 42% of named partners were identified and evaluated.

The observation that social relations and interdependency play a part in health is not surprising. But what network theory teaches us is that connections, even within the most complex systems, are not random (that is, they are not unpredictable). Instead, networks behave in ways that we can theorize, model, and predict.

In network analysis, the network becomes more important than the individual entity.

In its simplest form, network analysis can map ties between entities (whether elephants, humans, or genes). The same principles that allowed researchers to characterize the role of matriarchs in the social organization of the endangered African elephant species [3] also illuminated the collective dynamics fueling individual donations to the 2004 tsunami relief fund [4], and provided the techniques to model the gene network that controls T cell activation in humans [5].

But beyond identifying simple links, network analysis also helps to illustrate the structure of those ties—the nature of the relationships, the rules that govern them, and how we might predict various relationships or outcomes under various conditions. The network becomes more important than the individual entity. In this month’s PLoS Medicine paper by Lewis and colleagues [6], for example, investigating the transmission network (and its episodic nature) provides insights into HIV prevention that would not emerge from studying individual behavior.

Lewis and colleagues conducted their study because of a seeming contradiction. Genetic studies of HIV transmission networks have not corresponded well with the social contact networks revealed through interview data. The authors’ use of phylodynamics—a mix of genetics, epidemiology, and evolutionary biology—allowed a more sophisticated look. By examining and dating the genetic sequences of men attending an HIV clinic in central London, Lewis and colleagues found large clusters comprising ten or more individuals, a quarter of whom had transmitted the virus within several months of being infected. This information is valuable for understanding HIV transmission dynamics, not least because rapid transmission within clusters may result in the spread of drug-resistant strains.

Network analysis is also used in Mossong and colleagues’ study on the dynamics of influenza transmission, reported in this month’s PLoS Medicine [7]. Using paper diaries completed by over 7,000 Europeans documenting their daily physical and nonphysical contacts, Mossong and colleagues found varied mixing patterns, duration of contacts, and types of contacts. This information allowed the researchers to produce a mathematical model that suggests that 5–19-year-olds will suffer the highest burden of respiratory infection during any initial spread. Mossong and colleagues’ work illustrates how the patterning of social contacts—between and within groups, and in different social settings—and not just contact rates can influence how new emerging diseases spread. Physical exposure to an infectious agent, the authors conclude, is thus best modeled by taking into account the social network of close contacts and its patterning.

The physicist Albert-László Barabási argues in his book, Linked, that “there is a path between any two neurons in our brain, between any two companies in the world, between any two chemicals in our body. Nothing is excluded from this highly interconnected web of life” [8]. As health professionals, we might find network analysis useful in helping us describe and explain the connections in matters of health, whether they be at the cellular or population level. But we will also want to act.

Indeed, the greatest value in understanding networks lies in what they can tell us about taking action. The same insights generated from social network analysis about the spread of disease hold the key to developing effective interventions to halt that spread. The nature of social networks that drive transmission of syphilis and other sexually transmitted infections, for example, demonstrate that the Internet is an appropriate place to deliver safe sex education [9–11]. Exploiting the peer influences that feed the social network of obesity (or smoking, or substance abuse) could be a meaningful way to spread healthy behaviors. Even in diseases that appear intractable to our campaigns and controls, we might best inform policy makers and health promoters by considering: It’s the network, stupid.

References

1. Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. N Engl J Med 357: 370–379. Find this article online
2. Klausner JD, Wolf W, Fischer-Ponce L, Zolt I, Katz MH (2000) Tracing a syphilis outbreak through cyberspace. JAMA 284: 447–449. Find this article online
3. McComb K, Moss C, Durant SM, Baker L, Sayialel S (2001) Matriarchs as repositories of social knowledge in African elephants. Science 292: 491–494. Find this article online
4. Schweitzer F, Mach R (2008) The epidemics of donations: Logistic growth and power-laws. PLoS ONE 3: e1458. doi:10.1371/journal.pone.0001458.
5. Palacios R, Goni J, Martinez-Forero I, Iranzo J, Sepulcre J, et al. (2007) A network analysis of the human t-cell activation gene network identifies jagged1 as a therapeutic target for autoimmune diseases. PLoS ONE 2: e1222. doi:10.1371/journal.pone.0001222.
6. Lewis F, Hughes GJ, Rambaut A, Pozniak A, Leigh Brown AJ (2008) Episodic sexual transmission of HIV revealed by molecular phylodynamics. PLoS Med 5: e50. doi:10.1371/journal.pmed.0050050. Find this article online
7. Mossong J, Hens N, Jit M, Beutels P, Auranen K, et al. (2008) Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med 5: e74. doi:10.1371/journal.pmed.0050074. Find this article online
8. Barabási A-L (2003) Linked: How everything is connected to everything else and what it means New York: Plume. 304 p.
9. Benotsch EG, Kalichman S, Cage M (2002) Men who have met sex partners via the Internet: Prevalence, predictors, and implications for HIV prevention. Arch Sex Behav 31: 177–183. Find this article online
10. Bolding G, Davis M, Hart G, Sherr L, Elford J (2005) Gay men who look for sex on the Internet: Is there more HIV/STI risk with online partners. AIDS 19: 961–968. Find this article online
11. Curioso WH, Blas MM, Nodell B, Alva IE, Kurth AE (2007) Opportunities for providing web-based interventions to prevent sexually transmitted infections in Peru. PLoS Med 4: e11. doi:10.1371/journal.pmed.0040011. Find this article online

It’s the Network, Stupid: Why Everything in Medicine Is Connected

The PLoS Medicine Editors

 

Citation: The PLoS Medicine Editors (2008) It’s the Network, Stupid: Why Everything in Medicine Is Connected. PLoS Med 5(3): e71 doi:10.1371/journal.pmed.0050071

Published: March 25, 2008

Copyright: © 2008 The PLoS Medicine Editors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The PLoS Medicine Editors are Virginia Barbour, Jocalyn Clark, Larry Peiperl, Emma Veitch, and Gavin Yamey.

E-mail: medicine_editors@plos.org

---

One need look no further than Facebook to appreciate the significance and power of social networking. (Even PLoS has its own thriving Facebook community, which you can join at http://www.facebook.com/group.php?gid=2401713690.) But social networking is about more than just friends reunited; it’s a framework for understanding even the most basic of biological processes. Two papers in this month’s PLoS Medicine illustrate the insight that network theory brings to basic science, and the valuable interdisciplinarity that social network analysis can inspire.

Once the domain of social scientists—who have used social network analysis to study such diverse phenomena as kinship ties, organizational behavior, rumor spreading, and global air traffic—network theory has now entered the purview of health scientists. Network theory is concerned with mapping the links between entities, and social network analysis is the application of that theory to the social sciences. Searching for more social and environmental explanations for the obesity epidemic in America, for example, Christakis and Fowler [1] showed that obesity can spread from person to person, and that this spread depends on the nature of social ties: a person’s chance of becoming obese increased by 171% if he or she had a mutual friend who had become obese (even if they lived far away). Their risk increased by 40% if it was their sibling or spouse who became obese. Christakis and Fowler concluded that the social network is a crucial component—perhaps more so than genetics—in explaining obesity, a problem normally thought of as solely biological and behavioral.

Similarly, a major advance in stemming an outbreak of early syphilis in San Francisco was accomplished through understanding social networks. Klausner and colleagues found that the outbreak was tied to a network of sexual contacts who were meeting through Internet chat rooms [2]. The public health department was then able to initiate an electronic awareness and partner notification campaign using the same Web-based sexual network; 42% of named partners were identified and evaluated.

The observation that social relations and interdependency play a part in health is not surprising. But what network theory teaches us is that connections, even within the most complex systems, are not random (that is, they are not unpredictable). Instead, networks behave in ways that we can theorize, model, and predict.

In network analysis, the network becomes more important than the individual entity.

In its simplest form, network analysis can map ties between entities (whether elephants, humans, or genes). The same principles that allowed researchers to characterize the role of matriarchs in the social organization of the endangered African elephant species [3] also illuminated the collective dynamics fueling individual donations to the 2004 tsunami relief fund [4], and provided the techniques to model the gene network that controls T cell activation in humans [5].

But beyond identifying simple links, network analysis also helps to illustrate the structure of those ties—the nature of the relationships, the rules that govern them, and how we might predict various relationships or outcomes under various conditions. The network becomes more important than the individual entity. In this month’s PLoS Medicine paper by Lewis and colleagues [6], for example, investigating the transmission network (and its episodic nature) provides insights into HIV prevention that would not emerge from studying individual behavior.

Lewis and colleagues conducted their study because of a seeming contradiction. Genetic studies of HIV transmission networks have not corresponded well with the social contact networks revealed through interview data. The authors’ use of phylodynamics—a mix of genetics, epidemiology, and evolutionary biology—allowed a more sophisticated look. By examining and dating the genetic sequences of men attending an HIV clinic in central London, Lewis and colleagues found large clusters comprising ten or more individuals, a quarter of whom had transmitted the virus within several months of being infected. This information is valuable for understanding HIV transmission dynamics, not least because rapid transmission within clusters may result in the spread of drug-resistant strains.

Network analysis is also used in Mossong and colleagues’ study on the dynamics of influenza transmission, reported in this month’s PLoS Medicine [7]. Using paper diaries completed by over 7,000 Europeans documenting their daily physical and nonphysical contacts, Mossong and colleagues found varied mixing patterns, duration of contacts, and types of contacts. This information allowed the researchers to produce a mathematical model that suggests that 5–19-year-olds will suffer the highest burden of respiratory infection during any initial spread. Mossong and colleagues’ work illustrates how the patterning of social contacts—between and within groups, and in different social settings—and not just contact rates can influence how new emerging diseases spread. Physical exposure to an infectious agent, the authors conclude, is thus best modeled by taking into account the social network of close contacts and its patterning.

The physicist Albert-László Barabási argues in his book, Linked, that “there is a path between any two neurons in our brain, between any two companies in the world, between any two chemicals in our body. Nothing is excluded from this highly interconnected web of life” [8]. As health professionals, we might find network analysis useful in helping us describe and explain the connections in matters of health, whether they be at the cellular or population level. But we will also want to act.

Indeed, the greatest value in understanding networks lies in what they can tell us about taking action. The same insights generated from social network analysis about the spread of disease hold the key to developing effective interventions to halt that spread. The nature of social networks that drive transmission of syphilis and other sexually transmitted infections, for example, demonstrate that the Internet is an appropriate place to deliver safe sex education [9–11]. Exploiting the peer influences that feed the social network of obesity (or smoking, or substance abuse) could be a meaningful way to spread healthy behaviors. Even in diseases that appear intractable to our campaigns and controls, we might best inform policy makers and health promoters by considering: It’s the network, stupid.

References

1. Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. N Engl J Med 357: 370–379. Find this article online
2. Klausner JD, Wolf W, Fischer-Ponce L, Zolt I, Katz MH (2000) Tracing a syphilis outbreak through cyberspace. JAMA 284: 447–449. Find this article online
3. McComb K, Moss C, Durant SM, Baker L, Sayialel S (2001) Matriarchs as repositories of social knowledge in African elephants. Science 292: 491–494. Find this article online
4. Schweitzer F, Mach R (2008) The epidemics of donations: Logistic growth and power-laws. PLoS ONE 3: e1458. doi:10.1371/journal.pone.0001458.
5. Palacios R, Goni J, Martinez-Forero I, Iranzo J, Sepulcre J, et al. (2007) A network analysis of the human t-cell activation gene network identifies jagged1 as a therapeutic target for autoimmune diseases. PLoS ONE 2: e1222. doi:10.1371/journal.pone.0001222.
6. Lewis F, Hughes GJ, Rambaut A, Pozniak A, Leigh Brown AJ (2008) Episodic sexual transmission of HIV revealed by molecular phylodynamics. PLoS Med 5: e50. doi:10.1371/journal.pmed.0050050. Find this article online
7. Mossong J, Hens N, Jit M, Beutels P, Auranen K, et al. (2008) Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med 5: e74. doi:10.1371/journal.pmed.0050074. Find this article online
8. Barabási A-L (2003) Linked: How everything is connected to everything else and what it means New York: Plume. 304 p.
9. Benotsch EG, Kalichman S, Cage M (2002) Men who have met sex partners via the Internet: Prevalence, predictors, and implications for HIV prevention. Arch Sex Behav 31: 177–183. Find this article online
10. Bolding G, Davis M, Hart G, Sherr L, Elford J (2005) Gay men who look for sex on the Internet: Is there more HIV/STI risk with online partners. AIDS 19: 961–968. Find this article online
11. Curioso WH, Blas MM, Nodell B, Alva IE, Kurth AE (2007) Opportunities for providing web-based interventions to prevent sexually transmitted infections in Peru. PLoS Med 4: e11. doi:10.1371/journal.pmed.0040011. Find this article online

Eficacia del tratamiento quirurgico de la estenosis de canal lumbar

Weinstein JN, Tosteson TD, Lurie JD, Tosteson ANA, Blood E, Hanscom B et al. Surgical versus Nonsurgical Therapy for Lumbar Spinal Stenosis. N Engl J Med 2008; 358: 794-810. R TC (s) PDF (s)

Introducción

La estenosis del canal lumbar es una de las principales indicaciones de la cirugía lumbar en edades avanzadas. Sin embargo, se dan diferencias importantes entre diferentes áreas geográficas en la frecuencia con que se indica. En parte puede deberse a que los estudios de intervención sobre el tema son pequeños e incluían tanto pacientes con espondilolistesis como sin espondilolistesis.

Objetivo

Estudiar la eficacia del tratamiento quirúrgico de la estenosis de canal lumbar comparado con el tratamiento médico en pacientes sin espondilolistesis.

Perfil del estudio

Tipo de estudio: Ensayo clínico

Área del estudio: Tratamiento

Ámbito del estudio: Comunitario

Métodos

En el estudio Spine Patients Outcomes Research Trial (SPORT) se incluyeron una cohorte de pacientes distribuidos aleatoriamente a recibir tratamiento quirúrgico o tratamiento médico y una cohorte observacional de los pacientes que rechazaron la distribución aleatoria. El estudio se llevó a cabo en 13 centros médicos de 11 estados de EEUU. Se invitó a participar a pacientes con una historia de claudicación neurógena de al menos 12 semanas de evolución con pruebas de imagen que confirmaban la estenosis del canal lumbar a ≥1 nivel. Se excluyó a los pacientes con inestabilidad lumbar.

A los participantes se les ofreció entrar en una de las dos cohortes. En la cohorte de distribución aleatoria se distribuyó al azar a los pacientes a recibir tratamiento quirúrgico (laminectomía descompresiva posterior) o tratamiento médico (que debía incluir fisioterapia, educación sobre ejercicios para llevar a cabo en el domicilio y AINE).

Las variables de resultado principal fueron los subcuestionarios de dolor corporal y de función global del SF-36 y del Owestry Disability Index modificado medidos a las 3 semanas, 3 y 6 meses y 1 y 2 años. Las variables secundarias fueron la mejoría, la satisfacción con los síntomas actuales y con la atención recibida informados por el paciente y el grado de estenosis y de dolor lumbar. Se llevó a cabo un análisis por intención de tratar y en función del tratamiento recibido.

Resultados

Participaron en el estudio 654 pacientes (fig. 1). Las características iniciales de los participantes de los diferentes grupos fueron similares. La edad media fue de 65 años, un 61% eran varones y el 83% eran de raza blanca. El el 57% de los casos la duración de los síntomas era superior a los 6 meses. El síntoma predominante era la claudicación neurógena (80%). Una cuarta parte presentaban una asimetría en los reflejos. El nivel en el que se daba con mayor frecuencia la estenosis era L4-L5 y un 60% de los casos presentaban estenosis a más de un nivel. En más de la mitad de los casos la estenosis era grave. Un mínimo del 83% de los participantes aportaron datos en cada uno de los seguimientos.

Un porcentaje significativo de los pacientes incluidos inicialmente en el grupo de tratamiento no quirúrgico acabaron operándose (fig. 2).

En el análisis por intención de tratar, en la cohorte aleatoria se detectó una reducción significativa en la escala de dolor a los 2 años, sin que se encontrasen diferencias significativas en las otras variables. En cambio, en el análisis en función del tratamiento recibido, las diferencias fueron significativas para todas ellas independientemente de si se trataba de la cohorte observacional o de la aleatoria (fig. 3).

Dolor corporal
Función física
Incapacidad

Conclusiones

Los autores concluyen que los pacientes a los que se les practicó una intervención quirúrgica presentaron mejores resultados en todas las variables analizadas que los que fueron tratados por otros medios.

Conflictos de interés

Ninguno declarado. Financiado parcialmente por los National Institutes of Health y la A. J. and Sigismunda Palumbo Foundation.

Comentario

La estenosis del canal lumbar es la principal indicación de cirugía vertebral en >65 años. La mayor parte de los casos son de causa degenerativa. En los estudios radiológicos llevados a cabo sobre la población general se ha detectado un 20% de las personas >60 años de edad presentan una estenosis lumbar asintomática. En los pacientes con síntomas el cuadro más frecuente es el de la claudicación neurógena. En un estudio llevado a cabo en pacientes con dolor lumbar, la presencia de claudicación neurogena tiene una sensibilidad del 88% y una especificidad del 34%. Los síntomas de los pacientes acostumbran a mejorar al sentarse (sensibilidad del 46% y especificidad del 93%), empeoran con la extensión de la espalda y mejoran con la flexión. La maniobra de Romberg provoca una alteración de la estática que tiene una elevada especificidad. El cuadro se confirma mediante pruebas de imagen como el TAC o la RMN.

La mayor parte de los pacientes sintomáticos no presentan mejorías espontáneas y puede llegar a comprometer de forma importante la autonomía y la calidad de vida de los pacientes, por lo que se recomienda tratar el cuadro. El problema es que no se dispone de estudios correctamente diseñados sobre el tema. Entre los tratamientos no quirúrgicos se encuentran los ejercicios de flexión lumbar y para reforzar la musculatura abdominal los tratamientos analgésicos y las inyecciones epidurales de corticoides. El tratamiento quirúrgico de elección es la laminectomía con parcial facetectomía con o sin artrodesis lumbar. Los estudios sobre la eficacia de esta técnica también son escasos y contradictorios. Otra opción quirúrgica es la implantación de una prótesis que separe dos apófisis espinosas de forma que fuerce una flexión anterior de la vértebra que alivie la compresión.

El diseño inicial de este estudio es correcto, pero la baja adherencia al tratamiento del ensayo clínico hace que las conclusiones del mismo sean menos definitivas de lo esperado. Sin embargo, las elevadas diferencias observadas entre los pacientes operados y los tratados con otros medios, hace que la cirugía sea una opción a considerar en pacientes con sintomatología importante y que no han respondido al tratamiento médico.

Bibliografía

1. Katz JN, Harris MB. Lumbar Spinal Stenosis. N Engl J Med 2008; 358: 818-825. TC (s) PDF (s)
2. Khean Jin Goh, Waël Khalifa, Philip Anslow, Tom Cadoux-Hudson, Michael Donaghy. The Clinical Syndrome Associated with Lumbar Spinal Stenosis. 2004; 52: 242-249. R TC (s) PDF (s)

Autor

Manuel Iglesias Rodal. Correo electrónico: mrodal@menta.net.

Que es un centro de salud?

Fuente: Lista Medicina General Argentina. Luis Maria Ali Brouchoud. Obras Completas 1–Teoria del Hospital-Arquitectuta y Administracion

“…Sin pretender analizar en detalle, el pensamiento y la obra del Dr Ramón Carrillo, si queremos detenernos en algunos puntos fundamentales, ya que como expresáramos previamente encontramos a nivel nacional, la primera conceptualización sobre centros de salud y si asumimos que la primera descripción teórica completa sobre el gerenciamiento eficiente de los Servicios de Salud fue    concluida    en    los    años   20   por   Dawson   en   Inglaterra (Dawson/OPS,1964), siendo por primera vez mencionado el término “Centro de Salud Primario” como puerta de entrada del individuo en búsqueda de Servicios de Salud, podríamos afirmar el carácter de precursor e innovador de Carrillo.

Algunas citas de su libro “Teoría del Hospital” nos darán una clara idea de la altura de su pensamiento sanitario, más allá del tema que nos convoca:

 

-“Personalmente aspiro a algo más para el hospital. Estoy decidido a que, Dios mediante, los hospitales argentinos no sean solo casas de enfermedad, sino casas de la salud, de acuerdo con la nueva orientación de la medicina, la cual tiende a evitar que el sano se enferme, o a vigilar al sano para tomarlo al comienzo de cualquier padecimiento cuando este es fácilmente curable….”

 

-“Pero no es posible que todo sea obra del estado nacional; corresponde a las provincias, a los municipios y a los vecindarios identificarse con las necesidades y los grandes problemas de la salud pública…”

 

-“Pero no todo a de ser camas y hospitales. Un hospital bien organizado puede atender cinco veces más enfermos ambulatorios que internados. Todo depende de una eximia organización de los consultorios externos, fundada en la asistencia en equipo y en forma seriada…”

 

-“Recuerden ustedes que, de acuerdo con lo que dije del Centro Sanitario y de la Ciudad-Hospital, yo tiendo a eliminar del hospital los servicios externos, porque son un factor de perturbación. Deben estar –en lo posible- fuera del hospital, deben ubicarse en el centro sanitario que los proyectará en el ámbito social y no sobre el nosocomial…”(Carrillo R:,26,pág.13-15-16)

 

En el terreno específico de los centros de salud, las Normas de Ejecución de las Construcciones Hospitalarias, expresaban:

 

“Art. 1 Se entenderá por Centro de Salud , la unidad elemental de asistencia y medicina preventiva constituida por tres o cuatro consultorios polivalentes, con un equipo completo de rayos y destinados a la atención de enfermos, en general, y a la observación de los que no lo estuvieren y pertenecieren al personal de la administración nacional.

Art. 2. Se entenderá por Unidad Sanitaria, un conjunto de servicios ampliatorios de los asignados a los Centros de Salud. En tal sentido, se agregarán a dichos servicios otros de Tisiología, de Medicina Social, de Maternidad e Infancia y cualquier otro indispensable, de acuerdo con las características de la zona. Todos estos servicios tendrán sus servicios sociales correspondientes.

Art. 3. El Centro Sanitario será una organización superior a la Unidad Sanitaria y estará constituido por todos los consultorios externos necesarios para la atención polivalente integral de sanos y enfermos, completado, como en la Unidad Sanitaria, por un servicio social, con la centralización de la estadística sanitaria….”(Carrillo R.,26,pág.455)

Todo esto en el marco de las relaciones entre estas unidades periféricas que debían estar lo más cercano a la gente y la Ciudad-Hospital, con la cual debían estar adecuadamente conectados por medios de transporte específicos, dependiendo varios centros de salud de un hospital, elementos que todos conocemos como parte del concepto actual de regionalización  (este concepto incluía el área de cobertura, determinado por las camas del hospital de referencia y teniendo como mínimo la población a cubrir un mínimo de 2500 habitantes)

Encuesta sobre Historias Clinicas On Line

Julio Bonis Sanz, medico de familia de España, esta realizando una encuesta via internet que no lleva mas de 3 minutos realizarla. Es su interes recolectar los datos, pero en especial de personas que no estan dentro del ambito sanitario. La encuesta no implica dar datos personales, ni tampoco se deja un mail ni nada que este vinculado a posteriores utilizaciones de uso comercial, marketing, etc. En lo personal la acabo de hacer, y si alguien tiene dudas hagala, y si esta conforme pasela tambien a alguien que no sea del ambito sanitario para poder obtener la mayor cantidad de datos.
Sin duda, aparte de la encuesta en si, es interesante este nuevo instrumento para recolectar datos de opinión. Copio parte del mail recibido y el enlace desde donde se puede llenar la encuesta. Desde ya muchas gracias. RR

“Estoy realizando una encuesta anónima sobre Historias Clínicas
Personales Online (como por ejemplo www.keyose.com).

Si tienes un rato me gustaría que la respondieras aquí:

http://www.surveymonkey.com/s.aspx?sm=gjywHKL_2fd_2bt0pDZfIHcZQQ_3d_3d

Disculpa si te llega este mensaje por duplicado.” Se solicita su difusion.
Mas datos sobre Julio se pueden encontrar en

Curriculum vitae
http://www.juliobonis.com/main/

Personal Blog

http://gofiococido.blogspot.com