How do you know what to believe when it comes to medical research studies? What sources of information should you trust? What about statistics? Is evidence based medicine the sollution?
Auditing Medication errors in hospitalised patients at Chiradzulu and QECH Ho...Samson Rangford Chilambe
A proposal for Pharmacy year undergraduate research study for Samson Chilambe and Frank Chadewa. The proposal was approved by the COMREC hence the study was conducted at a small scale level in . Should funding be there, it shall be conducted at larger scale.
How do you know what to believe when it comes to medical research studies? What sources of information should you trust? What about statistics? Is evidence based medicine the sollution?
Auditing Medication errors in hospitalised patients at Chiradzulu and QECH Ho...Samson Rangford Chilambe
A proposal for Pharmacy year undergraduate research study for Samson Chilambe and Frank Chadewa. The proposal was approved by the COMREC hence the study was conducted at a small scale level in . Should funding be there, it shall be conducted at larger scale.
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).
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docxchristinemaritza
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evidence
Mollie R. Cummins
Ginette A. Pepper
Susan D. Horn
The next step to comparative effectiveness research is to conduct more prospective large-scale observational cohort studies with the rigor described here for knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) studies.
Objectives
At the completion of this chapter the reader will be prepared to:
1.Define the goals and processes employed in knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) designs
2.Analyze the strengths and weaknesses of observational designs in general and of KDDM and PBE specifically
3.Identify the roles and activities of the informatics specialist in KDDM and PBE in healthcare environments
Key Terms
Comparative effectiveness research, 69
Confusion matrix, 62
Data mining, 61
Knowledge discovery and data mining (KDDM), 56
Machine learning, 56
Natural language processing (NLP), 58
Practice-based evidence (PBE), 56
Preprocessing, 56
Abstract
The advent of the electronic health record (EHR) and other large electronic datasets has revolutionized efficient access to comprehensive data across large numbers of patients and the concomitant capacity to detect subtle patterns in these data even with missing or less than optimal data quality. This chapter introduces two approaches to knowledge building from clinical data: (1) knowledge discovery and data mining (KDDM) and (2) practice-based evidence (PBE). The use of machine learning methods in retrospective analysis of routinely collected clinical data characterizes KDDM. KDDM enables us to efficiently and effectively analyze large amounts of data and develop clinical knowledge models for decision support. PBE integrates health information technology (health IT) products with cohort identification, prospective data collection, and extensive front-line clinician and patient input for comparative effectiveness research. PBE can uncover best practices and combinations of treatments for specific types of patients while achieving many of the presumed advantages of randomized controlled trials (RCTs).
Introduction
Leaders need to foster a shared learning culture for improving healthcare. This extends beyond the local department or institution to a value for creating generalizable knowledge to improve care worldwide. Sound, rigorous methods are needed by researchers and health professionals to create this knowledge and address practical questions about risks, benefits, and costs of interventions as they occur in actual clinical practice. Typical questions are as follows:
•Are treatments used in daily practice associated with intended outcomes?
•Can we predict adverse events in time to prevent or ameliorate them?
•What treatments work best for which patients?
•With limited financial resources, what are the best interventions to use for specific types of patients?
•What types of indi ...
APPLICATION OF DATA SCIENCE IN HEALTHCAREAnnaAntony16
About the application of data science in healthcare. Healthcare is an essential field that touches on people's lives in many ways, and it has been revolutionized by data science over the years. Data science has enabled healthcare providers to better understand patients' needs, identify the root causes of diseases, and design effective treatment plans.
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).
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docxchristinemaritza
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evidence
Mollie R. Cummins
Ginette A. Pepper
Susan D. Horn
The next step to comparative effectiveness research is to conduct more prospective large-scale observational cohort studies with the rigor described here for knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) studies.
Objectives
At the completion of this chapter the reader will be prepared to:
1.Define the goals and processes employed in knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) designs
2.Analyze the strengths and weaknesses of observational designs in general and of KDDM and PBE specifically
3.Identify the roles and activities of the informatics specialist in KDDM and PBE in healthcare environments
Key Terms
Comparative effectiveness research, 69
Confusion matrix, 62
Data mining, 61
Knowledge discovery and data mining (KDDM), 56
Machine learning, 56
Natural language processing (NLP), 58
Practice-based evidence (PBE), 56
Preprocessing, 56
Abstract
The advent of the electronic health record (EHR) and other large electronic datasets has revolutionized efficient access to comprehensive data across large numbers of patients and the concomitant capacity to detect subtle patterns in these data even with missing or less than optimal data quality. This chapter introduces two approaches to knowledge building from clinical data: (1) knowledge discovery and data mining (KDDM) and (2) practice-based evidence (PBE). The use of machine learning methods in retrospective analysis of routinely collected clinical data characterizes KDDM. KDDM enables us to efficiently and effectively analyze large amounts of data and develop clinical knowledge models for decision support. PBE integrates health information technology (health IT) products with cohort identification, prospective data collection, and extensive front-line clinician and patient input for comparative effectiveness research. PBE can uncover best practices and combinations of treatments for specific types of patients while achieving many of the presumed advantages of randomized controlled trials (RCTs).
Introduction
Leaders need to foster a shared learning culture for improving healthcare. This extends beyond the local department or institution to a value for creating generalizable knowledge to improve care worldwide. Sound, rigorous methods are needed by researchers and health professionals to create this knowledge and address practical questions about risks, benefits, and costs of interventions as they occur in actual clinical practice. Typical questions are as follows:
•Are treatments used in daily practice associated with intended outcomes?
•Can we predict adverse events in time to prevent or ameliorate them?
•What treatments work best for which patients?
•With limited financial resources, what are the best interventions to use for specific types of patients?
•What types of indi ...
APPLICATION OF DATA SCIENCE IN HEALTHCAREAnnaAntony16
About the application of data science in healthcare. Healthcare is an essential field that touches on people's lives in many ways, and it has been revolutionized by data science over the years. Data science has enabled healthcare providers to better understand patients' needs, identify the root causes of diseases, and design effective treatment plans.
Chapter 9 Patient Safety, Quality and ValueHarry Burke MD P.docxmccormicknadine86
Chapter 9: Patient Safety, Quality and Value
Harry Burke MD PhD
Learning Objectives
After reviewing the presentation, viewers should be able to:
Define safety, quality, near miss, and unsafe action
List the safety and quality factors that justified the clinical implementation of electronic health record systems
Discuss three reasons why the electronic health record is central to safety, quality, and value
List three issues that clinicians have with the current electronic health record systems and discuss how these problems affect safety and quality
Describe a specific electronic patient safety measurement system and a specific electronic safety reporting system
Describe two integrated clinical decision support systems and discuss how they may improve safety and quality
Patient Safety-Related Definitions
Safety: minimization of the risk and occurrence of patient harm events
Harm: inappropriate or avoidable psychological or physical injury to patient and/or family
Adverse Events: “an injury resulting from a medical intervention”
Preventable Adverse Events: “errors that result in an adverse event that are preventable”
Overuse: “the delivery of care of little or no value” e.g. widespread use of antibiotics for viral infections
Underuse: “the failure to deliver appropriate care” e.g. vaccines or cancer screening
Misuse: “the use of certain services in situations where they are not clinically indicated” e.g. MRI for routine low back pain
Introduction
Medical errors are unfortunately common in healthcare, in spite of sophisticated hospitals and well trained clinicians
Often it is breakdowns in protocol and communication, and not individual errors
Technology has potential to reduce medical errors (particularly medication errors) by:
Improving communication between physicians and patients
Improving clinical decision support
Decreasing diagnostic errors
Unfortunately, technology also has the potential to create unique new errors that cause harm
Medical Errors
Errors can be related to diagnosis, treatment and preventive care. Furthermore, medical errors can be errors of commission or omission and fortunately not all errors result in an injury and not all medical errors are preventable
Most common outpatient errors:
Prescribing medications
Getting the correct laboratory test for the correct patient at the correct time
Filing system errors
Dispensing medications and responding to abnormal test results
5
While many would argue that treatment errors are the most common category of medical errors, diagnostic errors accounted for the largest percentage of malpractice claims, surpassing treatment errors in one study
Diagnostic errors can result from missed, wrong or delayed diagnoses and are more likely in the outpatient setting. This is somewhat surprising given the fact that US physicians tend to practice “defensive medicine”
Over-diagnosis may also cause medical errors but this has been less ...
Chapter 9 Patient Safety, Quality and ValueHarry Burke MD P.docxtiffanyd4
Chapter 9: Patient Safety, Quality and Value
Harry Burke MD PhD
Learning Objectives
After reviewing the presentation, viewers should be able to:
Define safety, quality, near miss, and unsafe action
List the safety and quality factors that justified the clinical implementation of electronic health record systems
Discuss three reasons why the electronic health record is central to safety, quality, and value
List three issues that clinicians have with the current electronic health record systems and discuss how these problems affect safety and quality
Describe a specific electronic patient safety measurement system and a specific electronic safety reporting system
Describe two integrated clinical decision support systems and discuss how they may improve safety and quality
Patient Safety-Related Definitions
Safety: minimization of the risk and occurrence of patient harm events
Harm: inappropriate or avoidable psychological or physical injury to patient and/or family
Adverse Events: “an injury resulting from a medical intervention”
Preventable Adverse Events: “errors that result in an adverse event that are preventable”
Overuse: “the delivery of care of little or no value” e.g. widespread use of antibiotics for viral infections
Underuse: “the failure to deliver appropriate care” e.g. vaccines or cancer screening
Misuse: “the use of certain services in situations where they are not clinically indicated” e.g. MRI for routine low back pain
Introduction
Medical errors are unfortunately common in healthcare, in spite of sophisticated hospitals and well trained clinicians
Often it is breakdowns in protocol and communication, and not individual errors
Technology has potential to reduce medical errors (particularly medication errors) by:
Improving communication between physicians and patients
Improving clinical decision support
Decreasing diagnostic errors
Unfortunately, technology also has the potential to create unique new errors that cause harm
Medical Errors
Errors can be related to diagnosis, treatment and preventive care. Furthermore, medical errors can be errors of commission or omission and fortunately not all errors result in an injury and not all medical errors are preventable
Most common outpatient errors:
Prescribing medications
Getting the correct laboratory test for the correct patient at the correct time
Filing system errors
Dispensing medications and responding to abnormal test results
5
While many would argue that treatment errors are the most common category of medical errors, diagnostic errors accounted for the largest percentage of malpractice claims, surpassing treatment errors in one study
Diagnostic errors can result from missed, wrong or delayed diagnoses and are more likely in the outpatient setting. This is somewhat surprising given the fact that US physicians tend to practice “defensive medicine”
Over-diagnosis may also cause medical errors but this has been less.
Medical Informatics: Computational Analytics in HealthcareNUS-ISS
Presented by Dr Liu Nan, Senior Research Scientist and Principal Investigator, Singapore General Hospital at ISS Seminar: How Analytics is Transforming Healthcare on 31 Oct 2014.
Research methodologies that result in data collecting from the patient medica...Pubrica
Developing a precise data collection instrument, implementing a coding manual, and continual communication with research personnel are all tactics for collecting accurate patient medical records.
Learn More : https://bit.ly/3x9r0Va
Reference: https://pubrica.com/services/medical-data-collection/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
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Protocols and Evidence based Healthcare: information technology tools to support best practices in health care, information technology tools that inform and empower patients.
Data Science in Healthcare" by authors Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic is an insightful guide that delves into the intersection of data science and healthcare. As a first-year student in Pharmaceutical Management, I found this book to be a valuable resource for understanding how data-driven approaches are transforming the healthcare industry, offering fresh perspectives and practical insights for future professionals like myself.
The Learning Health System: Thinking and Acting Across ScalesPhilip Payne
A Learning Health System (LHS) can be defined as an environment in which knowledge generation processes are embedded into daily clinical practice in order to continually improve the quality, safety, and outcomes of healthcare delivery. While still largely an aspirational goal, the promise of the LHS is a future in which every patient encounter is an opportunity to learn and improve that patient’s care, as well as the care their family and broader community receives. The foundation for building such an LHS can and should be the Electronic Health Record (EHR), which provides the basis for the comprehensive instrumentation and measurement of clinical phenotypes, as well as a means of delivering new evidence at the patient- and population levels. In this presentation, we will explore the ways in which such EHR-derived phenotypes can be combined with complementary data across a spectrum from biomolecules to population level trends, to both generate insights and deliver such knowledge in the right time, place, and format, ultimately improving clinical outcomes and value.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
1. The Structure of Medical
Data
• Medicine is remarkable for its failure to
develop a standarized vocabulary and
nomenclature
• Issues of data retrieval and analysis are
confounded by discrepancies between
observers and data analysts
• Imprecission and a lack of standarized
vocabulary are problematic when we wish to
aggregate data recorded by multiple health
professionals
• EHR’s, their encoding must be able to
presume a specific meaning for the terms
2. Coding systems
Because of the needs to know about
health trends for populations and to
recognize epidemics in the early
stages, there are various health
reporting requirements for hospitals
and physicians.
Also reporting discharge
diagnosis, procedures performed on
pacients
The codes used must be well defined
3. Coding systems
Coding systems have limitations when
are applied in more general clinical
settings
Researchers have worked to develop
a unified medical language system
(UMLS), a common structure that ties
together the various vocabularies the
have been created
4. The Data Knowledge
Spectrum
A central focus in medical informatics
is the information base that constitutes
the substance of medicine
Three terms are frequently used to
describe the content of computed
based systems: data, information and
knowledge
A database is a collection of individual
observations without summarizing
analysis
5. Strategies of Medical Data
Selection and Use
All medical databases are basically
incomplete because they reflect the
selective collection of data by the
health care personnel
The challenge is to ask only questions
that are necessary, to perform only the
examinations that are required and to
record only pertinent data
6. The Hypothetico - Deductive
Approach
Studies of medical decision makers have
shown that strategies for data collection
and interpretation may be imbedded in
an interative process known as
hypothetico-deductive approach
The central idea is one of
seuqential, staged data
collection, followed by data interpretation
and the generation of
hypotheses, leading to hipothesis-
directed selection of the next most
appropiate data to be collected.
7. The Hypothetico - Deductive
Approach
Physicians refers to the set of active
hypothesis as the differential
diagnosis
Physicians have developed safety
measures to avoid missing important
issues. They are focused in four
categories:
◦ Past medical history
◦ Family history
◦ Social history
◦ Review of systems
8. The Hypothetico - Deductive
Approach
After finishing with the physical
examination the list of hipotheses may
be narrowed down sufficiently that the
physician can undertake specific
treatment
It often is necessary to gather aditional
data
The response of the patient to
treatment is itself a datum point that
may affect the hypotheses about a
patient’s illness
9. The relationship between data
and hypotheses
Observation evokes a hypothesis
Sensitivity: the likehood that a given
datum will be observed in a patient
with a given disease or condition
Specificity. An observation is highly
specific for a disease if it is generally
not seen in patients who do not have
that disease
10. The relationship between data
and hypotheses
The prevalence of a disease is simply
a measure of the frequency with which
the disease occurs in the population of
interest
The predictive value of a test is simply
the post-test probability that a disease
is present based on the results of a
test
11. Methods for selecting questions
and comparing tests
Given a set of current
hypotheses, how does the physician
decide what additional data should be
collected?
12. The Computer and Collection of
Medical Data
The need for data entry by physicians
has posed a problem for medical
computing systems since the earliest
days of the field
In some applications is possible for
data to be enterred automatically into
the computer
The use of touch-
screens, mouse, PDA’s can help to
reduce the resistance to computer use