Glosario eSalud | eHealth Glossary


Glosario sobre eSalud | eHealth Glossary

Navegue por el glosario usando este índice.

Especial | A | B | C | D | E | F | G | H | I | J | K | L | M | N | Ñ | O | P | Q | R | S | T | U | V | W | X | Y | Z | TODAS

Página:  1  2  (Siguiente)
  TODAS

D

Imagen de System Administrator

Data Analytics in Healthcare

de System Administrator - miércoles, 11 de marzo de 2015, 21:35
 

Data Analytics in Healthcare: Gain insights and take action

A new study commissioned by EMC asked federal agencies how big data can help them. Among the results published recently: The healthcare industry is chomping at the bit for data analytics. Because the innovative answers needed to improve patient experiences and the health of populations, while simultaneously reducing costs, comes from insights, trends, and clues hiding in big data.

“The underpinnings of EHRs need to be reconfigured to support the purposes of big data. ”

Dr. Karen DeSalvo

National Coordinator for HIT

Please read the attached whitepaper.

 

Imagen de System Administrator

Data Breach Reporting Requirements for Medical Practices

de System Administrator - viernes, 27 de febrero de 2015, 12:58
 

Data Breach Reporting Requirements for Medical Practices

Imagen de System Administrator

Data Breaches Put Fear Into Patients

de System Administrator - jueves, 12 de marzo de 2015, 21:23
 

Survey: Data Breaches Put Fear Into Patients

by Rajiv Leventhal 

Nearly half (45 percent) of surveyed patients reported that they are at least moderately concerned about a security breach involving their personal health information, according to new research from the Austin, Tx.-based electronic health record (EHR) selection group Software Advice.

When asked to list the reasons behind their level of concern, the highest percentage of respondents (47 percent) said they are concerned about becoming the victim of fraud or identity theft. Coming in a close second was patient worries about maintaining the privacy of their medical history, followed by a lack of trust in technology’s ability to keep their data safe, according to the survey.

As such, 21 percent are withholding personal health information from their doctors. While the majority of the sample (79 percent) said this “rarely or never” happens, it is significant (and unfortunate) that 21 percent of patients withhold personal information from their physicians specifically because they are concerned about a security breach, according to the researchers.

What’s more, only 8 percent of patients “always” read doctors’ privacy and security policies before signing them, and just 10 percent are “very confident” they understand them. Notice of Privacy Practices (NPPs) are written explanations of how a provider may use and share health information, and how patients can exercise their privacy rights.

Additionally, a combined 54 percent of respondents said they would be “very” or “moderately likely” to change providers as a result of their personal health information being accessed without their permission. While 28 percent said there is nothing their provider could do that would convince them to stay, the greatest percentage of respondents (37 percent) would stick with their doctor if they provided specific examples of how the practice’s security policies and procedures had improved after the breach. Many of those same patients (13 percent) specifically said they would want the provider to purchase new software that protectspatient data. A breach caused by staff misconduct was reported as the most likely reason for patients to switch providers.

“The results of our survey on patient fears indicate that much work must be done to restore patients’ faith indata security, the researchers concluded. “Practices should strive to create an atmosphere where patients see promise instead of potential risk when it comes to the way healthcare data is handled,” they said.

Topics:

  • PRIVACY/SECURITY
  • TECHNOLOGY
  • HIPAA
  • TECHNOLOGY

Link: http://www.healthcare-informatics.com

Imagen de System Administrator

Data De-Identification: Getting It Right

de System Administrator - sábado, 9 de agosto de 2014, 02:29
 

Khaled El Emam and Scot Ganow

Data De-Identification: Getting It Right

Listen Now

When patient data is used for secondary purposes, such as research, it must be de-identified. But is this process consistently reliable in protecting patient privacy?

A privacy attorney and an experienced researcher explain in an interview with Information Security Media Group that de-identification is reliable if specific methods, as spelled out under HIPAA, are actually used. Too often, they say, those de-identifying data don't do the job effectively because they fail to follow best practices and standards.

Two Methods

Only two methods of de-identification can be used to satisfy the HIPAA Privacy Rule's de-identification standard, explains privacy and security attorney Scot Ganow of the law firm Faruki Ireland & Cox P.L.L.

The "safe harbor" method calls for removing 18 identifiers from patient information, including patient names, ZIP codes, Social Security numbers and birthdates.

The second method, "expert determination," is a more flexible standard that allows professionals to calibrate data de-identification based on the context for which data will be released for secondary purposes, explains Khaled El Emam, senior scientist at the Children's Hospital of Eastern Ontario Research Institute and Director of the multidisciplinary Electronic Health Information Laboratory.

The expert determination method involves using an expert "with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable," according to federal guidance material on the subject (see De-Identification Guidance Offered).

De-identified data is considered HIPAA-compliant "and defensible" if either of these two-approved methods are used in de-identifying patient data, Ganow says.

Some privacy advocates complain that even HIPAA-compliant methods of data de-identification fall short, creating a risk that patients can be re-identified, especially if mistakes are made in the processes (see Sizing Up De-Identification Guidance).

But El Emam contends that privacy protection problems mainly arise when HIPAA guidance is not followed or is applied improperly. "Another mistake is applying only part of the standards. In that case, data is not going to be protected," he says.

"If you do a poor job with de-identification not based on standards, then it's easy for someone to reverse that. But if you do a good job, it's really hard to re-identify the data," El Emam contends.

One of the top reasons why data de-identification is sometimes done improperly is that there's a shortage of skilled individuals who know how to de-identify data according to best practices and standards, El Emam says. "There's a need to increase the pool of individuals who can do this work, he says.

Not Foolproof

But no method of de-identification is guaranteed to be 100 percent perfect. "When applying data de-identification methods in accordance to HIPAA, the standard is to have a very low risk of re-identification as opposed to saying something is completely de-identified," Ganow says. De-identification "doesn't happen in a silo. You have to think about: Who am I giving the data to? What's the purpose? What agreements and security do I have in place? It's not a silver bullet."

In the interview, Ganow and El Emam discuss:

  • Why de-identification is important to managing risk and ensuring patient privacy;
  • How the identities of patients with unusual and rare diseases, such as Ebola, can be protected;
  • How a shortage of skilled individuals is contributing to poorly de-identified data and why training programs and professional certifications can help.

Ganow is an attorney in the Dayton office of Faruki Ireland & Cox P.L.L. He had more than 10 years of corporate and compliance experience in Fortune 500 companies prior to becoming an attorney, including serving as a chief privacy officer for healthcare and pharmaceutical informatics companies. Ganow also holds the Certified Information Privacy Professional certification; has presented and written extensively on the topics of data protection and de-identification.,In addition to his work at the Children's Hospital of Eastern Ontario Research Institute, El Emam is founder and CEO of Privacy Analytics Inc., which offers enterprise software to safeguard data used for secondary purposes. Previously, Khaled formerly was a senior research officer at the National Research Council of Canada. He holds the Canada research chair in electronic health information at the University of Ottawa and is an associate professor on the faculty of medicine at the university. He has a PhD from the department of electrical and electronics engineering, King's College, at the University of London, England.

Link: http://www.healthcareinfosecurity.com/interviews/data-de-identification-getting-right-i-2412/p-2

RELATED CONTENT

RELATED WHITEPAPERS

Imagen de System Administrator

Data Deluge to Disrupt Healthcare This Decade

de System Administrator - martes, 18 de noviembre de 2014, 13:43
 

Exponential Medicine: Data Deluge to Disrupt Healthcare This Decade

BY JASON DORRIER

You can’t really boil down four days, 59 speakers, and a few lovely musical interludes into a single word—but here goes. If there was an overriding theme to this year’s Exponential Medicine it was, in my humble opinion, information.

In his opening talk, Peter Diamandis said health and medicine are poised to undergo a greater transformation than any other industry or field in the next decade. Of course, he meant treatments and technology will meaningfully advance. But more than that, it is the liberation of data that will make care more targeted, proactive, and effective.

To understand the future, however, it’s critical to understand where we are now.

The Problem

 

Vinod Khosla; Exponential Medicine.

Venture capitalist Vinod Khosla wrote way back in 2012that modern healthcare is more about the “practice of medicine than the science of medicine.” Diagnosis and treatment are more art than most will admit, and this is problematic because, by definition, 50% of all doctors are below average practitioners—acceptable in art, frightening in medicine.

Tens of thousands of ICU patients die annually due to misdiagnosis. Go to three different doctors and you’ll get three different diagnoses and plans for treatment, Khosla wrote. This isn’t to slander doctors, but to say most are faced with an impossible task.

Further, just as today’s doctors make life or death decisions on extremely limited information, researchers and scientists similarly draw broad conclusions from small datasets, a tiny slice of the population over a short period of time. Indeed, in her keynote on clinical trials, Dr. Laura Esserman noted that 70% of clinical trials fail.

This is likely, in part, because the studies informing those trials are not backed by information over broad populations but are instead handcuffed by over-specificity and a dearth of data.

The Promise

Today, information isn’t free. But liberating forces are massing on the horizon.

 

Ariel Garten wearing Muse headband; Exponential Medicine.

Sensor technology, of course, is front and center. A profusion of body sensors are poised to be strung throughout the environment and in and on our bodies. These sensors are tiny, cheap, energy efficient, and most importantly, connected.

Sensors stand to collect information, not once every year or two, but every day, hour, or minute. They can open a window on disease before it becomes critical, before symptoms drive us to seek help, making diagnoses early and more accurate.

The best known health devices adapt smartphone motion sensors to detect movement (e.g., step trackers). But these are just the beginning. The next wave of sensors will measure a range of vital signs connected to the heart, blood, and brain.

Sensors on display at Exponential Medicine included two elegant EEG devices for recording brain activity, the Muse headband and iBrain. And the winner of the XPRIZE Nokia Sensing Challenge, awarded at the conference, is a compact system capable of running a wide range of diagnostic lab tests with a single drop of blood.

Just as sensors begin collecting new information, we may begin unlocking and leveraging already existing data within the system. Hospitals alone offer a wealth of information which is invisible to patients and doctors alike.

The system has all but scrambled this information, but data scientists are showing how software can piece it back together and make it useful.

Dr. Isaac Kohane told the story of a group of patients seeking recurrent treatments for various injuries. Using software to analyze the pattern of treatments, Kohane made a surprising diagnosis—domestic abuse. Indeed, it was later reported that these patients were victims of abuse, but not until well after they’d been released from the hospital.

Kohane believes a lot more such information exists within hospitals, if only anyone cared to look.

In addition to doctors, researchers may use information from sensors and the system itself to study populations of tens or hundreds of thousands of patients. And these studies will cover periods of time before, during, and after disease strikes.

The famous Framingham heart study collected information every few years from a few thousand patients over several decades. Framingham yielded profound insights into cardiac disease. Now, imagine doing the same study again—only collecting information every day and expanding the study’s population by an order of magnitude or more.

The Health eHeart study, spearheaded by UCSF’s Dr. Jeff Olgin aims to do just that. Health eHeart shows not just what’s possible in the future study of heart disease, but in the study of all disease. Broad, detailed data may soon be the rule.

Making It Meaningful

Doctors are already overwhelmed by the flow. Keeping up with a body of research that doubles every five years is a herculean task—perhaps an impossible one for mere mortals. How will we fare when information exponentially increases?

 

Craig Venter; Exponential Medicine.

As genomics and synthetic biology pioneer Craig Ventersaid in his keynote talk, data isn’t the goal. The bigger objective is taking the data and making knowledge of it. How will we do that? Artificial intelligence.

Vinod Khosla believes computers will replace up to 80% of the tasks doctors perform today. This will result in significantly fewer errors, lower cost, less work per doctor, faster interactions, and more opportunities for doctors to do research.

But, as Exponential Medicine executive director Daniel Kraft noted: We shouldn’t think of it as AI but IA—intelligence augmentation. In the future, doctors will pair up with intelligent software to more quickly and comprehensively research, diagnose, and prescribe treatment plans.

IBM’s Watson, for example, is able to scan a field’s entire body of up-to-date medical research in fractions of a second and turn up relevant studies, rare drug side effects, even potential diagnoses. And as Watson searches text, machine learning techniques are equipping software with the ability to scan images.

Jeremy Howard, Founder and CEO of Enlitic and previous Chief Scientist at Kaggle, said the accuracy of object classification—identifying discrete features in images—has undergone massive improvement in the last several years. Already these algorithms are proving themselves superior to humans in the analysis of some cancerous tissues.

The convergence of these techniques will help us better manage all this new information—whether it’s finding causative correlations in genomic research or making more accurate, timely diagnoses in the doctor’s office and hospital.

But perhaps the most powerful effect of intelligent software on medicine? As machines do what they do best, doctors can refocus on what humans do best. Less overwhelmed by data they can’t possibly digest, doctors will find time to build relationships with the patient—answering questions, keeping them informed, making them comfortable.

The Dark Side

 

Marc Goodman; Exponential Medicine.

As more patient data is collected and made available for study and diagnosis, and more devices connect to the internet, health information will present a target for exploitation—if it’s online, it’s hackable.

According to Marc Goodman, typical identity theft is worth $2,000 to the thief—medical identity theft is worth more like $20,000. So far in 2014, medical cybercrime is up 600% because, Goodman says, it’s an easy target.

The answer isn’t to halt innovation but to pay more attention to security and enforcement. Goodman suggests some simple solutions: switching passwords on every website, securing connections to public networks, data encryption—and most critically, perhaps, taking care what information is shared online.

Technology as a Tool

Technology is amoral, it’s what humans do with it that determines whether it is a force for good or evil. In the coming years, we’ll have ample opportunity to adapt to a world awash in health information. We may decide to place severe limits on what and how information is shared. But the likelier outcome? The benefits of information sharing will outweigh the risks.

“We’ve gone from a data-poor world, to a data-rich world,” Larry Smarr told participants. “I’ve been through a lot of fields in my life. This is about as excited as I’ve been for research and what it’s going to do to change our lives.”

Image Credit: Shutterstock.com

RELATED TOPICS: 

Link: http://singularityhub.com

Imagen de System Administrator

Data-driven scheduling predicts patient no-shows

de System Administrator - jueves, 11 de septiembre de 2014, 14:55
 

Data-driven scheduling predicts patient no-shows

By Michael B. Farrell

With all the advancements in health care, the medical profession still cannot get its appointment book in order.

Doctors are constantly overbooked. Patients constantly rescheduling. One day a waiting room is packed, the next it’s empty.

So when Gabriel Belfort attended a health care hackathon at the Massachusetts Institute of Technology in 2012, he challenged the coders, engineers, and clinicians there to fix that nagging issue.

“There’s a scheduling problem in medicine,” said Belfort, who at the time was a postdoctoral student studying brain science at MIT. “If you’ve had an appointment and you’ve showed up on time, you’ve probably had to wait.”

That dilemma posed by Belfort generated a very MIT proposal: What if you could use data science to determine which patients are likely to show up and which ones will be no-shows and manage office appointments around those tendencies?

“It was immediately clear to me that this is a problem that computers could solve,” Belfort said.

In short order, Belfort and an ad hoc team of nine people — students and health care professionals — at the hackathon built a prototype to prove out the concept. Then, so excited by the prospect that they could solve one of health care’s chronic pains, Belfort and three others who were strangers before that weekend launched a startup, aptly named Smart Scheduling Inc.

Here’s the gist: Smart Scheduling mines patient scheduling histories to determine who is more likely to cancel or miss an appointment. It then sends alerts to the scheduling programs that doctor offices use to book appointments.

If a patient is in a high-risk category, for instance, it prompts office schedulers to call with a reminder. If the patient cannot be reached, there is a good chance he will not show up at all. So, the doctors could then book another patient for that time slot, keeping the patient flow consistent throughout the day.

Within months of forming, Smart Scheduling attracted the interest of Healthbox, an accelerator program that invests $50,000 in promising startups and gives them free office space and mentoring. It also landed a meeting with executives at athenahealth Inc., which eventually resulted in Smart Scheduling’s becoming the first startup in the Watertown health information company’s new accelerator program. Athenahealth also made an undisclosed investment to help the company build out its marketing and sales efforts.

So far, Smart Scheduling has attracted some $500,000 in early-stage investment.

 

And already it has two large health systems signed up as customers: Martin’s Point Health Care, which runs health centers in Maine, and Steward Health Care System, one of the biggest hospital groups in Massachusetts, where the software is being used by about 40 of its doctors offices.

Dr. Michael Callum, president of Steward Medical Group, said Smart Scheduling helps take some of the ambiguity and guesswork out of making appointments; by eliminating unexpected down time, Steward doctors systemwide are able to see 100 more patients every week.

“When you leave it to the front-desk people in the office, they are not all that good of predicting flow in terms of when patients will show up,” Callum said. “It turns out that Smart Scheduling is much better at predicting that.”

Here is what Smart Scheduling has learned about us as patients: If we are single, or under 40, we are more likely to cancel an appointment than an older or married patient. New patients miss more appointments than regulars.

In general, expecting patients to show up for the 1 p.m. slot is a bad idea. On the other hand, Wednesdays are great, as patients are not likely to cancel on those days.

So far, Smart Scheduling has developed 722 variables that it uses to make predictions, based on an analysis of millions of data points about patients from athenahealth. And the more data Smart Scheduling can crunch, the better it gets at predicting behavior

The company says that, so far, its analysis has proven accurate 70 percent of the time when predicting cancellations.

“If everybody got a better schedule, we’d all be happier,” said Ateet Adhikari, director of the Healthbox accelerator program. “The patients benefit, the doctors benefit, and the insurer benefits. A more efficient system trickles down.”

Smart Scheduling was among the first companies that Healthbox invested in when it launched in Boston in 2012. Since then, it has backed 19 health-related startups.

Smart Scheduling exemplifies a new type of health care startup; instead of going after the big issues in health care — curing cancer, for instance — they are targeting more modest changes to improve the medical experience with technology.

“Companies like Smart Scheduling are dramatically improving health care not by producing a new drug,” said Bill Aulet, director of the Martin Trust Center For MIT Entrepreneurship. “It’s by streamlining the process and getting increased efficiencies.”

Belfort has since gone on to work at a local biotech company, although he remains an adviser to Smart Scheduling. Out of the group that came together to build the original product at the MIT hackathon in 2012, only Chris Moses has stuck around full time, and is now the company’s chief executive.

Improving patient flow in the doctor’s office is just the first step, Moses said. “The next step,” he added, “is to try to figure out who are the sickest patients and who the ones are that need to be seen first.”

Link: http://www.bostonglobe.com

Imagen de System Administrator

De-identification effective in maintaining patient privacy if done right

de System Administrator - sábado, 9 de agosto de 2014, 01:28
 

De-identification effective in maintaining patient privacy if done right

By Katie Dvorak

As hospitals and healthcare organizations adopt new ways to store and share data, privacy and security of the information is a top priority--and with that comes de-identification of data.

When it comes to HIPAA, there are two standards that allow for the sharing of data while maintaining privacy protections, according to privacy attorney Scot Ganow and Khaled El Emam, senior scientist at the Children's Hospital of Eastern Ontario Research Institute, both of whom spoke with HealthcareInfoSecurity.com.

The first HIPAA method for de-identifying data, according to Ganow, of Faruki Ireland & Cox, is to strip out the data and identifiable elements, though, he added that doing so doesn't offer a lot of value. The second, he said, is to de-identify data through the expert determination standard, which allows researchers to "retain a lot of the value of the info ... [while] at the same time carrying a very low risk of re-identification."

Emam, who also serves as the director of the multidisciplinary Electronic Health Information Laboratory at the Children's Hospital institute, also emphasized using the expert determination method, saying it allows for more flexibility.

He told HealthcareInfoSecurity.com that not every organization uses the standards, and in those cases, the data won't be protected.

In addition to HIPAA, the Federal Trade Commission also has de-identification standards, including that an organization takes reasonable steps to de-identify protected data and announces that re-identification of data will not occur.

However, some are not sure that de-identification goes far enough in protecting patients.

Some studies have shown the possible ease with which de-identified data can be linked with a patient, including one by Harvard University researchers who were able to identify and link anonymous participants in a public DNA study with their personal data.

And while HIPAA specifies how data should be de-identified, a report by the Bipartisan Policy Center maintains that too much variability exists in the execution of anonymization.

Emam, though, said that if the process is done right, it is very difficult to re-identify data. He stressed that problems occur when organizations do a "lousy job" with de-identification, and that makes it easy for someone to reverse.

To learn more:
- listen to the HealthcareInfoSecurity.com interview

Related Articles:

Panel: Cloud's role in healthcare still up in the air
Anonymous research patients easily re-identified, Harvard researchers find
HIPAA burdensome to big data healthcare efforts, BPC says

Read more about: Federal Trade Commission, Deidentification

 

Imagen de System Administrator

Delivering Healthcare on an iPhone

de System Administrator - martes, 16 de septiembre de 2014, 21:05
 

Delivering Healthcare on an iPhone

Joseph Kvedar at TEDxMidAtlantic

Joseph C. Kvedar, MD, is the Founder and Director of the Center for Connected Health, creating a new model of healthcare delivery, by developing innovative strategies to move care from the hospital or doctor's office into the day-to-day lives of patients. Dr. Kvedar is creating innovative programs to leverage information technology -- cell phones, computers, networked devices and simple remote health monitoring tools -- to help providers and patients manage chronic conditions, maintain health and wellness and improve adherence, engagement and clinical outcomes. Based on the technology platform developed at the Center, Healthrageous, a personalized health technology company, was launched in 2010, offering a range of health and wellness self-management programs to their clients.

In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations).

Top 10 Essential iPhone Apps for Doctors and Medical Students

by Giriraj Ranawat

 

#1. Heart Pro III

Paid – $2.99

This app, offered by 3D4 Medical in conjunction with Stanford University School of Medicine, allows users to rotate, cut, and label different components of a realistic 3D heart. This latest version contains many improvement which include, complete 360 degree 3D horizontal degree rotation of any body part with a swipe of your finger, 19 free & 51 paid animations, 2 types of Quiz, 62 images isolating elements of the heart.

#2. Epocrates

Free

This app is freely available across many mobile platforms including., Android, Blackberry and iPhone. Epocrates is a trusted clinical resource which helps in better patient care by delivering right information at the time it is required. More than 1 Million active members including 50 per cent US Physicians rely on this app to provide accurate and effective solution to there regular problems. It also perform dozens of calculations, such as BMI and GFR and timely medical news and research information.

#3. Medscape

Free

Medscape is used by more than 3 million healthcare professionals throughout the world and was the most downloaded app in Medical category in the year 2010. Developed by WebMD, Medscape provides Medical news and critical alerts in 34 specialty areas. It has a large pool of clinical resource which includes., 4,000+ evidence-based articles, 600+ step-by-step procedure videos, 100+ tables & protocols and Medical Calculators.

#4. Anesthesiology i-pocketcards

Paid – $3.99

Developed by Börm Bruckmeier Publishing, this app is a concise clinical reference guide with a compilation of scores, classifications, algorithms, and dosage information necessary for the anesthesiologist’s environment. It also contains an effective fluids and electrolytes management system, a table of anti-coagulation and neuraxial anesthesia, and special information about cardiothoracic and obstetric anesthesia.

#5. Eye Chart

Free

This is a great little app when you’re triaging, and can’t get the formal eyechart posted somewhere else in your department. Eye chart is used by eye care professionals and others to measure visual acuity. Snellen charts are named after the Dutch ophthalmologist Herman Snellen who developed the chart in 1862.

#6. Doximity

Free

Meant for US Physicians only, this app proves to be an effective and reliable medium to communicate and keep up with other medical peers. There are already 1 lac Doctors on this network and is considered as the most powerful medical directory and communication tool in the world. This app can really help you in building your Social and Professional Network if you are a newbie in Medical field.

#7. The ECG Guide

Paid – 55 INR

ECG Guide is a reservoir if around 200 examples of common and uncommon ECGs. It also incorporates ECG Interpreter which help sin stepwise assistance with ECG interpretation. You can also test your knowledge with 100 multiple-choice quiz questions updated regularly.

#8. MedCalc

Paid – 110 INR

This Medical Calculator helps to sort out complex medical calculations and problems using a simple UI. MedCalc continues to be the best and trusted medical calculator of all time for medical personnels providing easy access to complicated medical formulas, scores, scales and classifications. It features a Customizable list of favorite equations too.

#9. Psych Terms

Free

Verbal skills are must for any profession and the same goes for medical science too, with access to 1000+ frequently used psychiatric and mental health terms, phrases and definitions, Psych Terms continues to remain a quick and handy reference for both physicians and students. Surprisingly, these all words are available offline i.e., you don’t need a internet or Wi-Fi connection to access the resource.

#10. Pocket Lab Values

Paid – $2.99

Mathematical figures and values are quiet complicated and tedious to remember, so a ready reference to this stuff is necessary. Pocket Lab Values is the perfect companion for health professionals with access to over 320 common and uncommon lab values. Despite, of many apps providing the same feature, Pocket Lab Values contains more lab values than any other app on the store because of the consistent effort and feedback it takes from its users.

Does any of your favourite app made a miss from the list ?? It will be highly appreciated if you report the same to us and which will subsequently help us in mending the article too. 

 

Giriraj Ranawat

Giriraj Ranawat is a passionate tech blogger from Rajasthan, India. He is an avid traveler and love to explore new dimensions of technology. You can follow him at Twitter

 

 

Imagen de System Administrator

Depresión en el anciano

de System Administrator - jueves, 9 de octubre de 2014, 10:55
 

Un cuadro complejo, con comorbilidades y alto riesgo

Depresión en el anciano

La depresión en el anciano es frecuente y conlleva mayor riesgo de suicidio que en otros grupos etarios. Hay tratamientos eficaces, como los inhibidores selectivos de la recaptación de serotonina (ISRS) y la psicoterapia.

Audio

Resumen

La depresión en personas mayores de 60 años es frecuente y a menudo se asocia con enfermedades coexistentes, disfunción cognitiva o ambas. Los ancianos con depresión tienen mayor riesgo de suicidio.

La farmacoterapia o la psicoterapia se pueden emplear como tratamiento de primera línea. Los antidepresivos son eficaces para los ancianos, pero éstos pueden tener mayor riesgo de efectos colaterales. Los inhibidores selectivos de la recaptación de serotonina (ISRS) se consideran como tratamiento de primera línea.

La psicoterapia (conductual cognitiva o terapia de resolución de problemas) también es eficaz para la depresión en el anciano.

Cuadro clínico

La depresión en la vejez es la aparición de un trastorno depresivo mayor en adultos de 60 años o más. Se produce en hasta el 5% de los adultos mayores no institucionalizados y el 8 - 16% de los ancianos sufren síntomas depresivos clínicamente significativos. Las tasas de trastorno depresivo mayor aumentan cuando el paciente sufre otras enfermedades concomitante, hasta el 5-10% en atención primaria y hasta el 37% tras hospitalizaciones en cuidados intensivos.

En relación con los ancianos que refieren un episodio depresivo inicial en la juventud, aquéllos con depresión de inicio tardío son más proclives a sufrir trastornos neurológicos, entre ellos deficiencias en las pruebas neuropsicológicas y cambios relacionados con la edad mayores que lo normal en los estudios por imágenes; tienen también más riesgo de demencia ulterior. Estas observaciones generaron la hipótesis de que la enfermedad vascular puede contribuir a la depresión en algunos ancianos.

Tabla 1 Criterios Diagnósticos del DSM-5 para el trastorno depresivo mayor.
Deben estar presentes cinco o más de los siguientes síntomas casi todos los días durante dos semanas:

Síntomas principales (≥1 necesarios para el diagnóstico).

* El DSM-5 es el Manual Diagnóstico y Estadístico de Trastornos Mentales, Quinta edición.

El problema clínico

El mal estado anímico puede ser menos frecuente en ancianos con depresión que en adultos más jóvenes deprimidos, mientras que la irritabilidad, la ansiedad y los síntomas somáticos suelen ser más frecuentes en ancianos. Los factores psicosociales estresantes, como la muerte de un ser querido, pueden desencadenar un episodio depresivo, aunque las reacciones transitorias a las pérdidas importantes pueden simular depresión.

Las personas con depresión en la vejez tienen mayores tasas de enfermedades concomitantes y por lo tanto de empleo de medicamentos, que los que no están deprimidos. La relación entre depresión y enfermedad coexistente puede ser bidireccional: problemas médicos como el dolor crónico pueden predisponer a la depresión y ésta a su vez se asocia con peor evolución de enfermedades como las cardiopatías. Las enfermedades concomitantes pueden generar polifarmacia, entre otros, los efectos de los psicotrópicos sobre algunas enfermedades y sobre el metabolismo de otros medicamentos.

El deterioro cognitivo es frecuente en ancianos con depresión. La depresión puede ser un factor de riesgo para el deterioro cognitivo y una manifestación del mismo: se asocia con el aumento a largo plazo de demencia. Las deficiencias cognitivas pueden ser signos de envejecimiento cerebral acelerado que predispone y perpetúa la depresión.

Estrategias y evidencia

Evaluación
La U.S. Preventive Services Task Force recomienda la detección sistemática de la depresión si se cuenta con apoyo para asegurar el diagnóstico preciso y el tratamiento y el seguimiento apropiados. Para evaluar la depresión se deben emplear mediciones validadas, como el Patient Health Questionnaire 9, que refleja los criterios diagnósticos (véase tabla 1). Debido a que las tasas de suicidio son altas en los ancianos, especialmente en los hombres, es necesario explorar cuidadosamente la existencia de pensamientos suicidas.

En la tabla 2 se resumen puntos importantes de los antecedentes

Son signos de alarma para la intervención urgente: síntomas graves o que empeoran, las tendencias suicidas y el deterioro del funcionamiento cotidiano.

Los exámenes complementarios recomendados son: hemograma para descartar anemia, glucemia, tirotrofina, ya que el hipotiroidismo puede imitar los síntomas depresivos. Se recomienda también medir las cifras de vitamina B12 y folato ya que la frecuencia de deficiencia de vitamina B12 aumenta con la edad y las cifras bajas de ésta y de folato pueden contribuir a la depresión.

La prueba cognitiva (e.g.,Mini–Mental State) se justifica para personas que refieren problemas de memoria y puede revelar deficiencias en el procesamiento visual espacial o la memoria, aún si la puntuación total está dentro de lo normal.

Tratamiento

Cambios en los hábitos de vida

Se debe estimular a los ancianos deprimidos a aumentar su actividad física en la medida de lo posible. En un metanálisis de siete estudios aleatorizados, controlados, el ejercicio de intensidad moderada redujo los síntomas depresivos. Otras recomendaciones son mejorar la alimentación y aumentar las actividades placenteras y las interacciones sociales. En general, debido a que la depresión aumenta la dificultad de iniciar cambios en los hábitos de vida, estas recomendaciones son insuficientes si no se efectúan farmacoterapia, psicoterapia o ambas.

Farmacoterapia

Debido a sus escasos efectos secundarios y su bajo costo, los inhibidores selectivos de la recaptación de serotonina (ISRS), son el tratamiento de primera línea para la depresión de la vejez. En algunos estudios aleatorizados, controlados, aunque no en todos, ISRS como la sertralina, la fluoxetina y la paroxetina fueron más eficaces que el placebo para disminuir los síntomas de depresión.

En general, los que mostraron un beneficio significativo en pacientes con depresión de la vejez fueron grandes estudios; por ejemplo, los estudios que mostraron que la sertralina es beneficiosa tuvieron más de 350 participantes en cada grupo. En los estudios más importantes, las tasas de respuesta a los ISRS (≥50% de reducción en la gravedad de la depresión) oscilaron entre el 35 y el 60%, mientras que la respuesta al placebo fue del 26 - 40%. Las tasas de remisión (síntomas depresivos mínimos) fueron del 32 -44% con los ISRS versus 19 - 26% con el placebo.

Los efectos adversos comunes de los ISRS, que suelen ser leves, son náuseas y cefalea. Pero preocupan informes que observan mayor riesgo de accidente cerebrovascular (ACV) entre personas que reciben ISRS que entre los que no los emplean. Se observó aumento similar del riesgo de ACV con otras clases de antidepresivos, para lo cual no hay una explicación evidente.

Los inhibidores de la recaptación de serotonina-norepinefrina (IRSN) se emplean como fármacos de segunda línea cuando no se logra remisión con los ISRS. Al igual que con estudios en adultos más jóvenes, los estudios aleatorizados con ancianos no mostraron diferencias significativas entre los beneficios de los ISRS y los de los IRSN, aunque los efectos adversos pueden ser más frecuentes con estos últimos.

Si los ISRS o los IRSN son ineficaces, se pueden considerar los antidepresivos tricíclicos, que tienen eficacia similar, si bien sus efectos colaterales son mayores. Los antidepresivos tricíclicos están incluidos en la lista de los Beers Criteria entre los medicamentos que pueden ser inapropiados por sus frecuentes efectos adversos en los ancianos.
Estudios abiertos y pequeños estudios controlados avalan el empleo de bupropión y mirtazapina en pacientes con depresión en la vejez, pero faltan estudios rigurosos controlados por placebo.

Tras ser autorizados para su empleo auxiliar en la depresión resistente al tratamiento, los antipsicóticos de segunda generación olanzapina y aripiprazol se emplean cada vez más para tratar la depresión no psicótica. Un análisis conjunto de subgrupos que incorporó datos de tres estudios controlados por placebo, la mayoría con adultos más jóvenes, mostró que entre pacientes de 50 - 67 años, las tasas de remisión con 6 semanas de refuerzo con aripiprazol fueron mayores que con refuerzo con placebo (32,5% vs. 17,1%). La acatisia fue el efecto colateral más común, en el 17% de los pacientes ancianos. Son necesarios datos a más largo plazo en estos pacientes.

Psicoterapia

La psicoterapia es eficaz para la depresión de la vejez y se la puede considerar como tratamiento de primera línea. Los enfoques terapéuticos son, entre otros, una fase de tratamiento breve, consistente en visitas semanales durante 8 - 12 semanas. Aunque otros tratamientos también pueden ser eficaces, la evidencia más fuerte a favor del tratamiento breve es la de la terapia conductual cognitiva y la terapia de resolución de problemas.

Poder generalizar, sin embargo, es difícil, porque la mayoría de los estudios de psicoterapia para la depresión de la vejez son en poblaciones geriátricas con cognición intacta, con buen nivel educativo, blancos y relativamente jóvenes.

La terapia conductual cognitiva se centra en identificar y reformular los pensamientos negativos, disfuncionales y al mismo tiempo aumentar la participación en tareas agradables y actividades sociales. Su efecto puede ser más débil en personas con enfermedades físicas o con deterioro cognitivo.

La terapia de resolución de problemas se centra sobre el desarrollo de aptitudes para mejorar la capacidad de afrontar los problemas. Estudios aleatorizados con ancianos mostraron que el tratamiento de resolución de problemas produce mayor mejoría de la depresión que la atención habitual o la terapia de reminiscencia, una psicoterapia centrada en la evaluación y la reformulación de episodios de la vida pasada.

La terapia de resolución de problemas es eficaz para tratar los síntomas depresivos en ancianos con deficiencias cognitivas (sobre todo disfunción ejecutiva), grupo que con frecuencia no responde bien a los antidepresivos. En un estudio de población con deficiencias cognitivas, la terapia de resolución de problemas produjo más remisiones que la terapia de apoyo (el 46% vs. el 28% a 12 semanas), así como también mayor mejoría de la discapacidad y mantuvo los beneficios durante por lo menos 24 semanas.

La terapia interpersonal para ancianos con depresión se centra en las transiciones de roles, la tristeza y las cuestiones interpersonales. En estudios aleatorizados esta terapia redujo mucho más los síntomas depresivos que el tratamiento habitual. Al igual que con la terapia cognitiva conductual, las personas con otras enfermedades concomitantes o con deficiencias cognitivas quizás no respondan bien a la terapia interpersonal.

Tratamiento de mantenimiento

Estudios longitudinales mostraron beneficios significativos del tratamiento continuo tras la remisión. Uno de ellos se efectuó con ancianos con depresión recurrente que tuvieron una remisión breve con nortriptilina y terapia interpersonal durante 16 semanas.

Se asignó aleatoriamente a los participantes a tratamiento de mantenimiento con nortriptilina o placebo y a una sesión mensual de psicoterapia (terapia interpersonal) o a ninguna psicoterapia. Tres años después, las tasas de recidiva fueron significativamente menores entre las personas asignadas a tratamiento continuo con nortriptilina sola (43%), nortriptilina y terapia interpersonal (20%), o terapia interpersonal sola (64%) que entre las que recibieron placebo sin terapia interpersonal (90%).

Sin embargo, en un estudio similar sobre pacientes con un primer episodio de depresión, el tratamiento de mantenimiento con paroxetina (sola o con terapia interpersonal), pero no con terapia interpersonal sola, disminuyó el riesgo de recidiva a 2 años, en relación con ningún tratamiento de mantenimiento. No hay datos de estudios aleatorizados a largo plazo para evaluar la eficacia del tratamiento de mantenimiento con terapia cognitiva conductual o terapia de resolución de problemas para la depresión del anciano.

Estimulación cerebral

El tratamiento electroconvulsivo (TEC) o electroshock es el tratamiento más eficaz para los pacientes con depresión intensa, incluidos los ancianos. Aunque los antidepresivos son el tratamiento de primera línea, el TEC se debe considerar si los pacientes son suicidas, no respondieron a los medicamentos antidepresivos, tienen un trastorno físico deteriorante o una discapacidad relacionada con la depresión que amenaza su posibilidad de vivir independientemente.

Datos de estudios abiertos, con pacientes que no respondieron a los antidepresivos, sugieren tasas de remisión del 70 - 90% con TEC. Faltan datos de estudios controlados de alta calidad con intervención simulada que empleen técnicas modernas de TEC. Estudios aleatorizados muestran altas tasas de recaída (40 - 50% en los 6 meses posteriores al tratamiento).

El TEC tiene pocas contraindicaciones. Los efectos colaterales más frecuentes son confusión con amnesia anterógrada y retrógrada; las técnicas actuales de administración disminuyen este riesgo y los síntomas cognitivos se resuelven tras finalizar el TEC. Las personas con enfermedad cardiovascular o neurológica tienen mayor riesgo de problemas de memoria relacionados con el TEC.

La estimulación magnética transcraneal (EMT) es un tratamiento más nuevo que emplea un campo electromagnético focal generado por una bobina situada sobre el cuero cabelludo, en general sobre la corteza prefrontal izquierda. Las sesiones se efectúan cinco veces a la semana durante 4 - 6 semanas.

Este tratamiento no tiene efectos secundarios cognitivos. Sin embargo, un metanálisis de seis estudios que compararon la EMT con el TEC mostraron que el TEC tiene mayores tasas de remisión. Algunos estudios sugieren que la respuesta en los ancianos deprimidos puede no ser tan positiva como la de los pacientes más jóvenes.

Dudas

Los datos sobre la eficacia y la seguridad de muchos antidepresivos en poblaciones ancianas son escasos o ausentes y quizás haya riesgos específicos para estas poblaciones. Los datos sobre la farmacoterapia prolongada y las estrategias de mantenimiento de la psicoterapia en poblaciones ancianas también son limitados.

No es evidente cuál es la mejor manera de tratar las deficiencias cognitivas en pacientes ancianos con depresión. Estas deficiencias son pronósticas de poca respuesta a los antidepresivos; aún con la remisión de la depresión, las deficiencias pueden persistir e indican un alto riesgo de demencia. Ni la memantina, autorizada para tratar la enfermedad de Alzheimer, ni los estimulantes como el metilfenidato mostraron beneficios cognitivos en pacientes ancianos con depresión.

Recomendaciones

Las recomendaciones de este trabajo coinciden con las de la American Psychiatric Association. Estas recomendaciones subrayan la necesidad de una cuidadosa evaluación del riesgo de suicidio y de las enfermedades concomitantes en esta población.

Conclusiones y recomendaciones

Para el primer episodio depresivo en un anciano el tratamiento de primera línea podría ser la farmacoterapia o la psicoterapia, según las preferencias del paciente y la disponibilidad de la psicoterapia. Si se emplean medicamentos, el tratamiento inicial recomendado es un ISRS, con una dosis baja al inicio (e.g., sertralina 25 mg/día) a fin de evaluar los efectos colaterales en el paciente y aumentar después a la dosis terapéutica mínima (50 mg/día).

Pueden ser necesarias dosis mayores para obtener la máxima eficacia (e.g., 100 mg o más de sertralina diariamente), con mucha atención a los efectos secundarios. Si los síntomas depresivos no disminuyen se podría considerara cambiar a un IRSN, como la venlafaxina. Se deben efectuar pruebas de detección para deficiencias cognitivas y considerar pruebas neuropsicológicas si los síntomas cognitivos persisten o empeoran a pesar del tratamiento antidepresivo.

REFERENCIAS

1. Blazer DG. Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci 2003; 58:249-65.
2. Lyness JM, Caine ED, King DA, Cox C, Yoediono Z. Psychiatric disorders in older primary care patients. J Gen Intern Med 1999; 14:249-54.
3. Jackson JC, Pandharipande PP, Girard TD, et al. Depression, post-traumatic stress disorder, and functional disability in survivors of critical illness in the BRAIN-ICU study: a longitudinal cohort study. Lancet Respir Med 2014; 2:369-79.
4. Alexopoulos GS, Young RC, Meyers BS. Geriatric depression: age of onset and dementia. Biol Psychiatry 1993; 34:141-5.
5. Alexopoulos GS, Meyers BS, Young RC, Campbell S, Silbersweig D, Charlson M. ‘Vascular depression’ hypothesis. Arch Gen Psychiatry 1997; 54:915-22.
6. Taylor WD, Aizenstein HJ, Alexopoulos GS. The vascular depression hypothesis: mechanisms linking vascular disease with depression. Mol Psychiatry 2013; 18: 963-74.
7. Jiang W, Alexander J, Christopher E, et al. Relationship of depression to increased risk of mortality and rehospitalization in patients with congestive heart failure. Arch Intern Med 2001; 161:1849-56.
8. Saczynski JS, Beiser A, Seshadri S, Auerbach S, Wolf PA, Au R. Depressive symptoms and risk of dementia: the Framingham Heart Study. Neurology 2010; 75:35-41.
9. Mojtabai R. Diagnosing depression in older adults in primary care. N Engl J Med 2014; 370:1180-2.
10. Conwell Y, Thompson C. Suicidal behavior in elders. Psychiatr Clin North Am 2008; 31:333-56.
11. Bridle C, Spanjers K, Patel S, Atherton NM, Lamb SE. Effect of exercise on depression severity in older people: systematic review and meta-analysis of randomized controlled trials. Br J Psychiatry 2012; 201:180-5.
12. Schneider LS, Nelson JC, Clary CM, et al. An 8-week multicenter, parallel-group, double-blind, placebo-controlled study of sertraline in elderly outpatients with major depression. Am J Psychiatry 2003; 160: 1277-85.
13. Sheikh JI, Cassidy EL, Doraiswamy PM, et al. Efficacy, safety, and tolerability of sertraline in patients with late-life depression and comorbid medical illness. J Am Geriatr Soc 2004; 52:86-92.
14. Raskin J, Wiltse CG, Siegal A, et al. Efficacy of duloxetine on cognition, depression, and pain in elderly patients with major depressive disorder: an 8-week, double-blind, placebo-controlled trial. Am J Psychiatry 2007; 164:900-9.
15. Tollefson GD, Bosomworth JC, Heiligenstein JH, Potvin JH, Holman S. A double-blind, placebo-controlled clinical trial of fluoxetine in geriatric patients with major depression. Int Psychogeriatr 1995;7: 89-104.
16. Rapaport MH, Schneider LS, Dunner DL, Davies JT, Pitts CD. Efficacy of controlled-release paroxetine in the treatment of late-life depression. J Clin Psychiatry 2003; 64:1065-74.
17. Bose A, Li D, Gandhi C. Escitalopram in the acute treatment of depressed patients aged 60 years or older. Am J Geriatr Psychiatry 2008; 16:14-20.
18. Kasper S, de Swart H, Friis Andersen H. Escitalopram in the treatment of depressed elderly patients. Am J Geriatr Psychiatry 2005; 13:884-91.
19. Roose SP, Sackeim HA, Krishnan KR, et al. Antidepressant pharmacotherapy in the treatment of depression in the very old: a randomized, placebo-controlled trial. Am J Psychiatry 2004; 161:2050-9.
20. Smoller JW, Allison M, Cochrane BB, et al. Antidepressant use and risk of incident cardiovascular morbidity and mortality among postmenopausal women in the Women’s Health Initiative study. Arch Intern Med 2009; 169:2128-39.
21. Oslin DW, Ten Have TR, Streim JE, et al. Probing the safety of medications in the frail elderly: evidence from a randomized clinical trial of sertraline and venlafaxine in depressed nursing home residents. J Clin Psychiatry 2003; 64:875-82. 22. Schatzberg A, Roose S. A doubleblind, placebo-controlled study of venlafaxine and fluoxetine in geriatric outpatients with major depression. Am J Geriatr Psychiatry 2006; 14:361-70. 23. Rosenberg C, Lauritzen L, Brix J, Jorgensen JB, Kofod P, Bayer LB. Citalopram versus amitriptyline in elderly depressed patients with or without mild cognitive dysfunction: a Danish multicentre trial in general practice. Psychopharmacol Bull 2007; 40:63-73.
24. American Geriatrics Society 2012 Beers Criteria Update Expert Panel. American Geriatrics Society updated Beers Criteria for potentially inappropriate medication use in older adults. J Am Geriatr Soc 2012; 60:616-31.
25. Weihs KL, Settle EC Jr, Batey SR, Houser TL, Donahue RM, Ascher JA. Bupropion sustained release versus paroxetine for the treatment of depression in the elderly. J Clin Psychiatry 2000; 61:196-202.
26. Roose SP, Nelson JC, Salzman C, Hollander SB, Rodrigues H, Mirtazapine in the Nursing Home Study Group. Open label study of mirtazapine orally disintegrating tablets in depressed patients in the nursing home. Curr Med Res Opin 2003; 19:737-46.
27. Hardy SE. Methylphenidate for the treatment of depressive symptoms, including fatigue and apathy, in medically ill older adults and terminally ill adults. Am J Geriatr Pharmacother 2009; 7:34-59.
28. Gerhard T, Akincigil A, Correll CU, Foglio NJ, Crystal S, Olfson M. National trends in second-generation antipsychotic augmentation for nonpsychotic depression. J Clin Psychiatry 2014; 75:490-7.
29. Sheffrin M, Driscoll HC, Lenze EJ, et al. Pilot study of augmentation with aripiprazole for incomplete response in latelife depression: getting to remission. J Clin Psychiatry 2009; 70:208-13.
30. Rutherford B, Sneed J, Miyazaki M, et al. An open trial of aripiprazole augmentation for SSRI non-remitters with late life depression. Int J Geriatr Psychiatry 2007; 22:986-91.
31. Steffens DC, Nelson JC, Eudicone JM, et al. Efficacy and safety of adjunctive aripiprazole in major depressive disorder in older patients: a pooled subpopulation analysis. Int J Geriatr Psychiatry 2011; 26: 564-72.
32. Kiosses DN, Leon AC, Areán PA. Psychosocial interventions for late-life major depression: evidence-based treatments, predictors of treatment outcomes, and moderators of treatment effects. Psychiatr Clin North Am 2011; 34:377-401.
33. Gould RL, Coulson MC, Howard RJ. Cognitive behavioral therapy for depression in older people: a meta-analysis and meta-regression of randomized controlled trials. J Am Geriatr Soc 2012; 60: 1817-30.
34. Pinquart M, Duberstein PR, Lyness JM. Effects of psychotherapy and other behavioral interventions on clinically depressed older adults: a meta-analysis. Aging Ment Health 2007; 11:645-57.
35. Crabb RM, Cavanagh K, Proudfoot J, Learmonth D, Rafie S, Weingardt KR. Is computerized cognitive-behavioural therapy a treatment option for depression in late-life? A systematic review. Br J Clin Psychol 2012; 51:459-64.
36. Arean PA, Perri MG, Nezu AM, Schein RL, Christopher F, Joseph TX. Comparative effectiveness of social problem-solving therapy and reminiscence therapy as treatments for depression in older adults. J Consult Clin Psychol 1993;61: 1003-10.
37. Areán PA, Raue P, Mackin RS, Kanellopoulos D, McCulloch C, Alexopoulos GS. Problem-solving therapy and supportive therapy in older adults with major depression and executive dysfunction. Am J Psychiatry 2010; 167:1391-8.
38. Alexopoulos GS, Kiosses DN, Heo M, Murphy CF, Shanmugham B, Gunning-Dixon F. Executive dysfunction and the course of geriatric depression. Biol Psychiatry 2005; 58:204-10.
39. Sheline YI, Pieper CF, Barch DM, et al. Support for the vascular depression hypothesis in late-life depression: results of a 2-site, prospective, antidepressant treatment trial. Arch Gen Psychiatry 2010; 67: 277-85.
40. Alexopoulos GS, Raue PJ, Kiosses DN, et al. Problem-solving therapy and supportive therapy in older adults with major depression and executive dysfunction: effect on disability. Arch Gen Psychiatry 2011; 68:33-41.
41. Mossey JM, Knott KA, Higgins M, Talerico K. Effectiveness of a psychosocial intervention, interpersonal counseling, for subdysthymic depression in medically ill elderly. J Gerontol A Biol Sci Med Sci 1996; 51:M172-M178.
42. Reynolds CF III, Dew MA, Pollock BG, et al. Maintenance treatment of major depression in old age. N Engl J Med 2006; 354:1130-8.
43. Reynolds CF III, Frank E, Perel JM, et al. Nortriptyline and interpersonal psychotherapy as maintenance therapies for recurrent major depression: a randomized controlled trial in patients older than 59 years. JAMA 1999; 281:39-45.
44. Prudic J, Olfson M, Marcus SC, Fuller RB, Sackeim HA. Effectiveness of electroconvulsive therapy in community settings. Biol Psychiatry 2004; 55:301-12.
45. Prudic J, Haskett RF, McCall WV, et al. Pharmacological strategies in the prevention of relapse after electroconvulsive therapy. J ECT 2013; 29:3-12.
46. George MS, Lisanby SH, Avery D, et al. Daily left prefrontal transcranial magnetic stimulation therapy for major depressive disorder: a sham-controlled randomized trial. Arch Gen Psychiatry 2010; 67:507-16.
47. Slotema CW, Blom JD, Hoek HW, Sommer IE. Should we expand the toolbox of psychiatric treatment methods to include repetitive transcranial magnetic stimulation (rTMS)? A meta-analysis of the efficacy of rTMS in psychiatric disorders. J Clin Psychiatry 2010; 71:873-84.
48. Lisanby SH, Husain MM, Rosenquist PB, et al. Daily left prefrontal repetitive transcranial magnetic stimulation in the acute treatment of major depression: clinical predictors of outcome in a multisite, randomized controlled clinical trial. Neuropsychopharmacology 2009; 34:522-34.
49. Pallanti S, Cantisani A, Grassi G, et al. rTMS age-dependent response in treatment- resistant depressed subjects: a minireview. CNS Spectr 2012; 17:24-30.
50. Reynolds CF III, Butters MA, Lopez O, et al. Maintenance treatment of depression in old age: a randomized, doubleblind, placebo-controlled evaluation of the efficacy and safety of donepezil combined with antidepressant pharmacotherapy. Arch Gen Psychiatry 2011; 68:51-60.
51. Gelenberg AJ, Freeman MP, Markowitz JC, et al. Practice guideline for the treatment of patients with major depressive disorder. 3rd ed. Washington, DC:American Psychiatric Association, 2010.

Link: http://www.intramed.net

Imagen de System Administrator

DevOps strategy for health site fueled by flash, CDM

de System Administrator - jueves, 16 de marzo de 2017, 23:26
 

DevOps strategy for health site fueled by flash, CDM

by Dave Raffo

Women's health website Lifescript 'transformed into a DevOps' shop after implementing all-flash arrays from Pure Storage and Actifio CDM.

Flash storage and copy data management helped women's health website Lifescript adopt a DevOps strategy that is crucial to its revenue generation.

All-flash vendor Pure Storage Inc. and copy data management (CDM) pioneer Actifio Inc. last year launched the Actifio AppFlash DevOps Platform, which runs Actifio software on Pure arrays. Lifescript had already been using Actifio and Pure in combination for years as an early proponent of both technologies. That combo formed a storage foundation for a DevOps strategy.

"We've always been chasing performance," Lifescript's CTO Jack Hogan said of his company's move to flash.

Lifescript started using flash in Hewlett-Packard 3PAR StoreServ hybrid arrays in 2012. The hybrid 3PAR arrays improved IOPS performance, but latency remained an issue. Hogan looked at Pure Storage's FlashArray in the early days of the all-flash startup and became one of its first 100 customers in 2013.

He cited Pure's built-in data deduplication as a major selling point, especially after he tested it and it ran without a performance hit. He said the data reduction made Pure more cost-effective for Lifescript.

"It was hard to part with 3PAR because we had a long-term relationship with them," he said. "But it became a no-brainer once we determined there was no performance hit introducing [Pure's] dedupe and compression. We were always constrained by latency, and HP was still struggling to get its data reduction in place."

HP -- now Hewlett Packard Enterprise following the Hewlett-Packard company split -- introduced an all-flash version of 3PAR around the time Lifescript switched over, but Hogan said it would cost about eight times what the Pure array costs for usable capacity.

All-flash arrays reduce latency, bump IOPS

"We're making real-time business decisions based on our ability to process data. And now, we're processing it so much faster."
Jack Hogan | CTO, Lifescript

Hogan said Lifescript sends up to 30 million emails a day to subscribers. The website runs complex data analytics to find relevant information to send to users. He said the Pure arrays decreased latency from 60 milliseconds to five milliseconds while running analytics, helping Lifescript pump out relevant information faster.

"We're making real-time business decisions based on our ability to process data. And now, we're processing it so much faster," Hogan said.

Lifescript has Pure FlashArrays running in separate data centers for a total of 92 TB of raw capacity. Hogan said the arrays give him a 3 1/2:1 data reduction ratio.

Flash, copy data management form DevOps strategy

Lifescript actually implemented Actifio before switching to all-flash. Lifescript started with an Actifio CDS data center appliance running at a managed third-party site. Now, it uses Actifio Sky virtual appliances to bridge primary and secondary data centers. Sky handles replication between Pure arrays, enabling disaster recovery for the company's VMware and Microsoft SQL Server applications.

"The primary reason [for going to Actifio]," Hogan said, "was the economies of copy data management, being able to spin up replicas, and move and replicate data easily."

Actifio's CDM fits into the DevOps strategy. Hogan said Actifio has made life easier on the six developers on Lifescript's 17-person IT team, allowing the team to quickly create copies of 10 TB-plus databases to use for development purposes.

"We've transformed into a DevOps shop," he said. "Our database group can quickly spin up copies of things, and our development group can run their new application against production-class platforms. We're not creating secondary block-level storage; we're just spinning up copies.

"That contention that used to exist between individual divisions in the IT group has gone away."

Next Steps

Link: http://searchstorage.techtarget.com


Página:  1  2  (Siguiente)
  TODAS