The document discusses evidence farming as an approach to evaluating mobile health (mHealth) applications and interventions. Evidence farming involves extracting evidence from care process data and manipulating care processes with flexible protocols. This allows generating hypotheses at the population level while maintaining some internal validity. The document proposes an open architecture called OpenmHealth to support evidence farming and crowdsourcing evidence through modules for usage analytics, randomized trials, individualized studies, and sharing findings. The goal is a learning community that can rapidly disseminate and iterate on evaluation methods that matter to improving mHealth.
What will it take for patients and clinicians to use data from mobile health apps and sensors in routine care? Watch how Linq, a new product from Open mHealth, offers a new "bring your own app" approach that puts the focus back on patients and clinicians rather than on technology.
What will it take for patients and clinicians to use data from mobile health apps and sensors in routine care? Watch how Linq, a new product from Open mHealth, offers a new "bring your own app" approach that puts the focus back on patients and clinicians rather than on technology.
Siloed thinking, practices and technology greatly undermines potential to advance research, treatments and cures for most diseases. This is a shot at a vision to address this challenge, starting with a disease called primary ciliary dyskinesia (PCD).
Principles of evidence based medicine.
EBM means integrating individual clinical expertise with the best available external evidence from systematic research.
The Green Park Collaborative (GPC) has developed a new tool to help health care decision makers confidently and consistently use Real World Evidence (RWE) when making tough coverage and care choices. Called RWE Decoder, the spreadsheet-based assessment tool lets users review and evaluate all existing studies and evidence for both rigor and relevance. Informed by these factors, users can assess study quality, and generate a visual summary to help gauge the evidence under review.
Published RWE studies developed from data-rich electronic medical records or medical claims data are increasingly available from health care systems. However, the quality of this research can vary widely, and payers, clinicians and other health care decision makers often dismiss it out of hand. RWE Decoder and its associated user guide and framework, offer a thoughtful approach to helping these decision makers assess whether RWE studies address their questions and can appropriately guide their choices.
The tool, user guide, and supporting white paper are available here: https://goo.gl/AhbHUw
Developing a Framework for In-country Impact Evaluations of Malaria Control E...MEASURE Evaluation
Presented by Jui Shah, MEASURE Evaluation/ICF International, as part of a symposium organized by MEASURE Evaluation and MEASURE DHS at the 6th MIM Pan-African Malaria Conference.
Siloed thinking, practices and technology greatly undermines potential to advance research, treatments and cures for most diseases. This is a shot at a vision to address this challenge, starting with a disease called primary ciliary dyskinesia (PCD).
Principles of evidence based medicine.
EBM means integrating individual clinical expertise with the best available external evidence from systematic research.
The Green Park Collaborative (GPC) has developed a new tool to help health care decision makers confidently and consistently use Real World Evidence (RWE) when making tough coverage and care choices. Called RWE Decoder, the spreadsheet-based assessment tool lets users review and evaluate all existing studies and evidence for both rigor and relevance. Informed by these factors, users can assess study quality, and generate a visual summary to help gauge the evidence under review.
Published RWE studies developed from data-rich electronic medical records or medical claims data are increasingly available from health care systems. However, the quality of this research can vary widely, and payers, clinicians and other health care decision makers often dismiss it out of hand. RWE Decoder and its associated user guide and framework, offer a thoughtful approach to helping these decision makers assess whether RWE studies address their questions and can appropriately guide their choices.
The tool, user guide, and supporting white paper are available here: https://goo.gl/AhbHUw
Developing a Framework for In-country Impact Evaluations of Malaria Control E...MEASURE Evaluation
Presented by Jui Shah, MEASURE Evaluation/ICF International, as part of a symposium organized by MEASURE Evaluation and MEASURE DHS at the 6th MIM Pan-African Malaria Conference.
Module 2 of the Oral Health Tutorial, a production of UT HSC Libraries.
This module focuses on evidence-based dental health. View this tutorial to learn how to define evidence-based dental public health, learn effective retrieval strategy, be able to critique the literature and apply it to public health dental practice.
This tutorial is copyright Lara Sapp and Julie Gaines.
Josephine Briggs, MD
Director
National Center for Complementary and Alternative Medicine
National Institutes of Health
Opening Keynote "Research in an IT Connected World: Building Better Partnerships – NIH and Health Care Systems"
The era of ‘Big Data’ has arrived for biomedical research, bringing with it immense challenges as well as spectacular opportunities. NIH is establishing major programs with the potential to transform the future of US biomedical research by building the capacities necessary for these challenges. These programs will strengthen research partnerships with health care systems and the IT networks that support them.
The Big Data to Knowledge (BD2K) initiative, to be launched in 2014, will implement a set of recommendations from the Data and Informatics Working Group to the Advisory Committee to the Director. Investments are planned to meet scientific needs to manage and utilize large complex datasets, including strengthening training, and investing in improved analysis methods and software development and dissemination. NIH is also evaluating strengthening data and software sharing policies, and the potential creation of catalogs of research data, and data/metadata standards.
The Common Fund’s Health Care Systems (HCS) Research Collaboratory program has the goal to strengthen the national capacity to implement cost-effective large-scale research studies by engaging major health care delivery organizations as research partners. The aim of the program is to provide a framework of implementation methods and best practices that will enable the participation of many health care systems in clinical research. Research conducted in partnership with health care systems is essential to strengthen the relevance of research results to health practice. Seven demonstration projects, currently in a feasibility phase, are developing detailed methods to implement rigorous randomized studies of questions of major public health impact. These studies, and the IT infrastructure that will make them possible, will be described in detail.
MIE Medical Informatics in Europe: European Federation for Medical Informatics (EFMI) annual meeting
Worklshop: Addressing Patient Adherence Issues by Engaging Enabling Technologies
Chair: Pei-Yun Sabrina Hsueh (IBM T.J. Watson Research Center)
Pei-Yun Sabrina HSUEHa, , Marion BALL b,a, Michael MARSCHOLLEKc, Fernando J. MARTIN-SANCHEZd , Chohreh PARTOVIANa, and Vimla PATELe
aIBM T.J. Watson Research Center, NY, USA
b John Hopkins University, MD, USA
c Hannover Medical School, Germany
d Melbourne Medical School, Australia
e Center for Cognitive Studies in Medicine and Public Health, The New York Academy, USA
Abstract One of the well known issues providers have contended with for many years is the issue of patients’ adherence to their care plans and medications outside clinical encounters. In this workshop, we review proof of concept studies using technology at the point of care to assess patient literacy and self-efficacy to provide timely intervention, remedy, and improvements in cost and quality. We focus on patient-generated information, including patient reported data and measurements from devices and sensors, as key to improving patient safety, gaining “meaningful use” data, improving patient centric care, and assisting providers in learning more about their patient needs to improve outcomes. We look into barriers to adherence, basic understanding of the patients and providers roles in improving adherence, and the use of technology to assist patients in staying on track. The participants will address their findings in the integration of patient-generated information into everyday life and clinical practice and share lessons learned from implementing these designs in practice. This workshop aims to share requirements for the next-generation healthcare systems, especially in areas where the explosive availability of patient-generated data is expected to make impacts.
evidence based practice is best for the people working with patients
ebp should be used by the heath care provider.
ebp based upon clinical experties
best research evidence
patient preference and values
Final Presentation of the Bergen Summer Research School 2010, course 4: Mobile Technologies for Global Health Research (presented on Friday, July 2 by Ali Habib, John Wesonga and Heather Zornetzer)
Innovative research approaches to improve evidence in global healthEmilie Robert
Presentation given at the Canadian Conference on Global Health in 2015 in Montreal, with Federica Fregonese, Pierre Minn, Emilie Robert and Georges -Chalers Thiebaut
Transforming Medicine Through Personalized Health Care at Ohio State Universi...Ryan Squire
Dr. Clay Marsh presented "Transforming Medicine Through Personalized Health Care at Ohio State University Medical Center" at the 2009 Personalized Health Care National Conference.
Dr. Marsh is leading the Ohio State University Center for Personalized Health Care to create the future of medicine to improve people’s lives through personalized health care.
This module was developed at the School of Public Health, University for the Western Cape for the Postgraduate Certificate in Public Health which was offered as a distance learning module between 2001 and 2008. Health Systems Research is an integral part of the vision for a quality, comprehensive, community-based, participatory and equitable system. This module aims to provide an introduction to the kinds of research conducted within a health system, the research designs and methods used, and how to develop a research protocol.
Author(s): Mickey Chopra, John Coveney
Institution(s): University of the Western Cape
This resource is part of the African Health Open Educational Resources Network: http://www.oerafrica.org/healthoer. The original resource is also available from the authoring institution at http://freecourseware.uwc.ac.za/
Creative Commons license: Attribution-Noncommercial-Share Alike 3.0
This module was developed at the School of Public Health, University for the Western Cape for the Postgraduate Certificate in Public Health which was offered as a distance learning module between 2001 and 2008. Health Systems Research is an integral part of the vision for a quality, comprehensive, community-based, participatory and equitable system. This module aims to provide an introduction to the kinds of research conducted within a health system, the research designs and methods used, and how to develop a research protocol.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
1. Evidence Farming 1 : Implications for Open Architecture Ida Sim, MD, PhD Director, Center for Clinical and Translational Informatics University of California San Francisco May 5, 2011 1 With thanks to Rich Kravitz MD, UC Davis and Naihua Duan, Columbia
2. Rephrasing “Does it Work?” (Complexes of) Exposures Outcome strength of association? individual population Increased breastfeeding Text4Baby
I’d like t talk to you today about the idea of evidence farming, with thanks to xxx and xxx for originating this concept.
When we ask “does it work?” we’re really asking about the strength of assocation between a given exposure, or combination of exposurs, and a given outcome. For example, is Text4Baby is associated with increased breastfeeding. Usually, we’re asking the question at the population level, is Text4Baby associated with increased breastfeeding for the 100,000-plus women who use it. At other times, we’re interested in the question at the individual level -- is Text4Baby associated with increased breatsfeeding for this particular woman?
Right now, the most common evaluation approach is the randomized controlled trial, in which a group of subjects are randomized to prespecified interventions and followed for prespecified outcomes. The goal is to confirm a hypothesis at the population level, for example that use of an asthma app will reduce ER visits in the following year. Although they have strong internal validity, RCTs are slow and difficult to fund and set-up, they are expensive, which means trials are often small and of short duration as we heard yesterday. And because of their highly structured and restricted nature, RCTs also often lack relevance to the real world.
Another common approach is data mining. Throw all the data from the electronic health record, or mHealth apps, together, and “mine” for associations between exposures and outcomes. The goal of data mining is to generate hypotheses at the population level, but if the exposures or outcomes you’re interested in weren’t already collected by the source systems, you’re out of luck. Most importantly though, this kind of data is observational data from the care process, which is not complete or systematic so whatever associations you find are going to have weak internal validity.
If you want to find out whether something works for a paricular individual, the only formal method is the N-of-1 study. How many of you heard of N-of-1 studies? Used them? This is a within-subject multiple crossover design that randomizes the individual to an alternating sequence of interventions, and measures outcomes along the way. So here, a user is randomized to using either the app, then not, then the app or the other way around. And peak flow is measured to see whether the app works for this patient or not. This method is complicated, analysis is difficult, little known, and not widely used. But there are examples of this method leading to improved outcomes and cost savings.
As different as these methods are, they all share a common attitude towards evidence, which is that evidence is something to be extracted from the care process, either by mining it from the data or directly manipulating the care process with rigid and pre-defined protocols. If this evidence extraction is done without much regard for the realities of real clinical practice, it can feel like…
evidence strip mining, not terribly friendly to patient or clinician.
An alternative frame is evidence farming, in which we think of evidence not as something to be extracted from the cre process, but as something to be cultivated in a sustainable way as part of the care process. We should involve patients and clinicians in the cultivation of evidence (using patient to include both kingdom of the well and the sickl), by testing interventions and outcomes that matter to us, so we have a personal stake in the findings.
In this framing, data mining for interesting associations is like taking your pig to the forest to look for truffles.
RCTs is industrial evidence farming, evidence by and for the masses. But it should be organic industrial farming so to speak, with RCTs that are what we call pragmatic, that have less stringent inclusion criteria that test common interventions used in common ways. We also need more RCTs that test adaptive treatment strategies, and that are able ot evaluate interventions like apps that change over the course of the study. And patients should have more input into the study design.
But one of the things we really want to know in mHealth is “does this app work for me??!” and for that we need to cultivate our own personal evidence gardens, where apps should have a built-in function for guiding me to systematically find out if something is working for me or not. And it’s a personal garden because I should choose which exposures I test, and which outcomes I track.
I have a patient whose passion is competitive ballroom dancing. She has moderate asthma and wants to minimize her use of inhaled steroids. Her N-of-1 study might look like this: testing standing use of Flovent steroid inhaler vs on an as needed basis, and seeing how that impacts on her dancing.
Crowdsourcing can help users find out what exposures and outcomes others are concerned about, and can also help researchers and developers discover more patient-centered outcomes.
In fact, just like we have a food macrosystem, we have an evidence macrosystem that like food, should be balanced, sustainable, and good for us. Right now, the mHealth evidence macrosystem is weak throughout. We need data liquidity so we can mine across larger more comprehensive data sets. We need more pragmatic and relevant RCTs, but perhaps most importantly, we need to develop and support new methods of evaluation that can generate more personal evidence more quickly. There are also methods for aggregating N-of-1 studies to fill in the gap between personal evidence and RCTs that Eric Hekler mentioned this morning.
And if we do develop these new methods, how can we do evaluation at scale? I would say, by building support for a robust evidence macrosystem right into a common open infrastructure for mhealth.
Currently, mHealth is built in a stovepipe manner with little data sharing and interoperability, each app siting in it’s own silo. Yesterday, we heard about some very successful approaches, but each of us will have to reimplement them in our own apps. This limits the efficiency and the impact of quality mHealth. Of course, traditional enterprise health IT is all about silos.
But let’s do something different in mHealth! You know, the Internet took off after it adopted what came to be called the hourglass model, where TCP/IP was the narrow waist that was standardized and made open, and that reduced duplication, spurred innovation both above and below that narrow waist, and spawned many commercial and non-profit ventures.
I’m working with Deborah Estrin, a computer scientist from UCLA on openmHealth.org, a project to catalyze a phase transition of mHealth from stovepipe to a narrow waist open architecture.
These shared modules would include modules for usage analytics. A data sharing platform so we can aggregate and mine data across apps. We need shard modules for supporting major components of RCTs. Modules for scripting and analyzing individualized N-of-1 protocols, and other novel analysis approaches.
We need support for using social media approaches for discovery of exposures and outcomes that matter, and shared libraries of validated measures and instruments, like the PROMIS instruments developed by the NIH. There’s also huge opportunity for developing and sharing measures to get at finer-grained mechanisms based on theoretical models of behavior change, etc.
Evaluation is an important part of our openmHealth architecture but not the only part. Other parts include data collection and presentation modules, other analysis and data management services, authoring tools and interfaces to external applications and devices, etc. Our approach is to define a component architecture with well-defined APIs, and to build a reference implementation so that we can start to “tip” mHealth away from silos into an open ecosystem. We are particularly interested in open for apps targeting underserved populations.
Our goal for mHealth evidence Is to establish a learning community coupled with an open technical architecture so that we can broadly, rapidly, and iteratively disseminate both evaluation methods and findings that matter. It is well within our power as a community to do this.