Data Driven Practice in e-MDs. This covers custom crystal reports from scratch, slicing and dicing data in Excel, Visualizing Data, and understanding that change isn't really a technical problem.
Seminar at Software University
Bulgaria, Sofia
13th October 2015
--This is a modified version of the original presentation. ( Additional slides have been added.)
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Pin the tail on the metric v00 75 min versionSteven Martin
This presentation shows a different approach to metrics. Instead of listing the Top 10 field-tested metrics, we first talk about goals as prerequisites for metrics. Next, we discuss characteristics of good and bad metrics. We end with walking through an activity called “Pin the Tail on the Metric,” a technique to facilitate the critical thinking needed to determine what types of metrics can help your organization discuss trade-offs, options, and ultimately make better forward-looking decisions.
Seminar at Software University
Bulgaria, Sofia
13th October 2015
--This is a modified version of the original presentation. ( Additional slides have been added.)
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Pin the tail on the metric v00 75 min versionSteven Martin
This presentation shows a different approach to metrics. Instead of listing the Top 10 field-tested metrics, we first talk about goals as prerequisites for metrics. Next, we discuss characteristics of good and bad metrics. We end with walking through an activity called “Pin the Tail on the Metric,” a technique to facilitate the critical thinking needed to determine what types of metrics can help your organization discuss trade-offs, options, and ultimately make better forward-looking decisions.
As an analyst, the start of a problem is a wonderful place. All your time is focussed on discovering what drives the business you work in, or what causes it the most pain. Then you build something that helps to fix the problem. That’s when your life becomes less exciting.
Every problem you solve comes with a reduction in your capacity for the new and exciting, and your time is filled with monitoring, reporting and tweaking.
We want to talk to you about how you can build a team, tools and a culture to allow your analysts the opportunity to focus on the things they do best and enjoy the most – resulting in them being able to fix more problems (and spend some time trying to work out what AI actually is).
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Design Thinking for Data Science #StrataHadoopIntuit Inc.
O'Reilly #StrataHadoop Presentation- George Roumeliotis
This talk describes a Design Thinking methodology for tackling Data Science projects. Be warned that the talk is not about machine learning, and it is not about user interfaces. It's about being an effective Data Science practitioner. The talk was originally presented in 2015 at the O'Reilly Strata Conference in San Jose, CA, by George Roumeliotis, a Data Scientist working at Intuit.
To view the presentation, visit: http://youtu.be/LQ9HWNtlggU
Presentation from the September 2010 Columbus Web Analytics Wednesday. The presenter was Tim Wilson of Resource Interactive. Download the presentation (PPT 2007) for notes embedded in the slides and some useful animations.
Why building dashboards that deliver results is vital to business? It is not just the looks, but a blend of efficiency and effectiveness that rule the roost. Business visualizations built to context are a competitive advantage.
From Lab to Factory: Or how to turn data into valuePeadar Coyle
We've all heard of 'big data' or data science, but how do we convert these trends into actual business value. I share case studies, and technology tips and talk about the challenges of the data science process. This is all based on two years of in-the-field research of deploying models, and going from prototypes to production.
These are slides from my talk at PyCon Ireland 2015
Publishing Strategic Technology for Association of Catholic PublishersCraig Miller
Association of Catholic Publishers presentation on best practice approach to technology application to the publishing enterprise. Relevant to all organizations for whom technology is a service.
Data science is not Software Development and how Experiment Management can ma...Jakub Czakon
Working on data science projects that are run as if they were software development can sometimes feel like trying to fit a square peg in a round hole. In this talk, I will explain why that happens and what people do to try and fix it. Lately, in the context of machine learning, the concept of experiment management, which treats ml experiments as first-class citizens, has been gaining a lot of traction. I will discuss what it is, what are the benefits of using it, and how you can apply it in your work to make run your projects more efficiently.
A lot of companies make the mistake of thinking that just hiring Data Scientists will lead to increased revenue or increased profit. For a company’s investment in Data Science to be successful the Data Scientists need to work on the right problems, with the right people, and with the right tools. In this presentation, I will talk about the lessons I have learned, and mistakes made in applying Data Science in commercial settings over the last 10 years. I will highlight what processes can increase the chances of Data Science investment being successful.
What's the Value of Data Science for Organizations: Tips for Invincibility in...Ganes Kesari
This session was delivered as an Open Colloquium on Apr 30th 2020 for the Master in Information program students. It was organized by the Rutgers School of Communication & Information.
The session covers 3 themes:
- How do enterprises and not-for-profit organizations gain value from data science?
- What are the biggest challenges in data science that professionals are unaware of? How can students translate that into learnings, to make themselves indispensable in the industry
- What's the impact of COVID-19 and the recession on data science industry? How will the data jobs be impacted?
Dashboards are Dumb Data - Why Smart Analytics Will Kill Your KPIsLuciano Pesci, PhD
Organizations of every size have access to data dashboard technology, yet none of the solutions have delivered on their hype and right now across the world executives and analysts are staring at a dashboard and thinking the same thing, ""so what?""
The failure of dashboards to deliver meaningful insights is inherent in their simplicity: they only show surface level information, and not the relationships between data points that really drive the fate of your organization.
But all is not lost! By combining the right mix of technology and human expertise in business strategy, research and data mining you can embrace the smart analytics movement, and start accessing insights that grow your company and your competitive position.
You can watch the accompanying webinar here: https://youtu.be/RdOcPxv9wLs
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
Data Con LA 2018 - What I’ve Learned About How Machines Learn by Luis Bitenco...Data Con LA
What I’ve Learned About How Machines Learn: My time at Microsoft, Workpop, Reddit and Novi! By Luis Bitencourt-Emilio, Co-Founder & CTO @ Novi Finance
A run through of ML at Reddit starting in 2006 and through modern day, through building the team, the data platform, the experiments being run and their results.
As an analyst, the start of a problem is a wonderful place. All your time is focussed on discovering what drives the business you work in, or what causes it the most pain. Then you build something that helps to fix the problem. That’s when your life becomes less exciting.
Every problem you solve comes with a reduction in your capacity for the new and exciting, and your time is filled with monitoring, reporting and tweaking.
We want to talk to you about how you can build a team, tools and a culture to allow your analysts the opportunity to focus on the things they do best and enjoy the most – resulting in them being able to fix more problems (and spend some time trying to work out what AI actually is).
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Design Thinking for Data Science #StrataHadoopIntuit Inc.
O'Reilly #StrataHadoop Presentation- George Roumeliotis
This talk describes a Design Thinking methodology for tackling Data Science projects. Be warned that the talk is not about machine learning, and it is not about user interfaces. It's about being an effective Data Science practitioner. The talk was originally presented in 2015 at the O'Reilly Strata Conference in San Jose, CA, by George Roumeliotis, a Data Scientist working at Intuit.
To view the presentation, visit: http://youtu.be/LQ9HWNtlggU
Presentation from the September 2010 Columbus Web Analytics Wednesday. The presenter was Tim Wilson of Resource Interactive. Download the presentation (PPT 2007) for notes embedded in the slides and some useful animations.
Why building dashboards that deliver results is vital to business? It is not just the looks, but a blend of efficiency and effectiveness that rule the roost. Business visualizations built to context are a competitive advantage.
From Lab to Factory: Or how to turn data into valuePeadar Coyle
We've all heard of 'big data' or data science, but how do we convert these trends into actual business value. I share case studies, and technology tips and talk about the challenges of the data science process. This is all based on two years of in-the-field research of deploying models, and going from prototypes to production.
These are slides from my talk at PyCon Ireland 2015
Publishing Strategic Technology for Association of Catholic PublishersCraig Miller
Association of Catholic Publishers presentation on best practice approach to technology application to the publishing enterprise. Relevant to all organizations for whom technology is a service.
Data science is not Software Development and how Experiment Management can ma...Jakub Czakon
Working on data science projects that are run as if they were software development can sometimes feel like trying to fit a square peg in a round hole. In this talk, I will explain why that happens and what people do to try and fix it. Lately, in the context of machine learning, the concept of experiment management, which treats ml experiments as first-class citizens, has been gaining a lot of traction. I will discuss what it is, what are the benefits of using it, and how you can apply it in your work to make run your projects more efficiently.
A lot of companies make the mistake of thinking that just hiring Data Scientists will lead to increased revenue or increased profit. For a company’s investment in Data Science to be successful the Data Scientists need to work on the right problems, with the right people, and with the right tools. In this presentation, I will talk about the lessons I have learned, and mistakes made in applying Data Science in commercial settings over the last 10 years. I will highlight what processes can increase the chances of Data Science investment being successful.
What's the Value of Data Science for Organizations: Tips for Invincibility in...Ganes Kesari
This session was delivered as an Open Colloquium on Apr 30th 2020 for the Master in Information program students. It was organized by the Rutgers School of Communication & Information.
The session covers 3 themes:
- How do enterprises and not-for-profit organizations gain value from data science?
- What are the biggest challenges in data science that professionals are unaware of? How can students translate that into learnings, to make themselves indispensable in the industry
- What's the impact of COVID-19 and the recession on data science industry? How will the data jobs be impacted?
Dashboards are Dumb Data - Why Smart Analytics Will Kill Your KPIsLuciano Pesci, PhD
Organizations of every size have access to data dashboard technology, yet none of the solutions have delivered on their hype and right now across the world executives and analysts are staring at a dashboard and thinking the same thing, ""so what?""
The failure of dashboards to deliver meaningful insights is inherent in their simplicity: they only show surface level information, and not the relationships between data points that really drive the fate of your organization.
But all is not lost! By combining the right mix of technology and human expertise in business strategy, research and data mining you can embrace the smart analytics movement, and start accessing insights that grow your company and your competitive position.
You can watch the accompanying webinar here: https://youtu.be/RdOcPxv9wLs
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
Data Con LA 2018 - What I’ve Learned About How Machines Learn by Luis Bitenco...Data Con LA
What I’ve Learned About How Machines Learn: My time at Microsoft, Workpop, Reddit and Novi! By Luis Bitencourt-Emilio, Co-Founder & CTO @ Novi Finance
A run through of ML at Reddit starting in 2006 and through modern day, through building the team, the data platform, the experiments being run and their results.
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
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
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.
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
263778731218 Abortion Clinic /Pills In Harare ,ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group of receptionists, nurses, and physicians have worked together as a teamof receptionists, nurses, and physicians have worked together as a team wwww.lisywomensclinic.co.za/
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
41. 30DAYS
to context connection
Use this practical guide to engage with visualization and build great
context connection skills over the next 30 days.
est.
minutes ACTION, Content, *web address completion
30 0
Mon
WEEK 1
READ Is Information Visualization the Next Frontier for Design? http://bit.ly/30Days-InfoViz
WATCH Hans Rosling shows the best stats you've ever seen http://bit.ly/30Days-Rosling
Tue READ Business Intelligence isn't a technical problem, it is a Social Problem http://bit.ly/30Days-Problem
Wed READ Who is Edward Tufte? http://bit.ly/30Days-Tufte
Thu READ The Economist: New Ways of Visualising Data http://bit.ly/30Days-Visualize
Fri PLAY New York Times Visualization Lab http://bit.ly/30Days-NYTimes
Mon
WEEK 2
WATCH Before trying to communicate information, first understand it. http://bit.ly/30Days-Information
READ Part 1 Foundation: Guide to Creating Dashboards People Love http://bit.ly/30Days-DashboardLove1
Tue READ The Best of Business Intellgience: Innovation at the Fringe http://bit.ly/30Days-BI
Wed READ Think Like a Designer http://bit.ly/30Days-Think
Thu DO 30 Resources to Find the Data you Need http://bit.ly/30Days-GetData
Fri PLAY Indexed. Fun with communication of Data http://bit.ly/30Days-Indexed
DO Create your own visualization http://bit.ly/30Days-ManyEyes
Geek vs Fuzzy Perspective.\nHacks vs Keywords\n\nLet’s get started\n
Healthcare = least analytical industry\nPathetically un-analytical\n
So, anyone here is probably an Early Adopter\n
With focus, early adoption advantage won’t last.\nWhy Health IT? Giga? Multipositive\n
Makes win-win possible. Better decisions & better insights.\nAnd only 4% even have the possibility of kicking up the heat.\nWho uses the mounds of data?\n\n
Most Rx’d. Order Tracking Efficiency. Scan’s fastest? 99213/4 Ratio.\nTaskman Bottleneck. Sign Off issues. Holdups on Refills. Overdue Coumadin.\nBest Continuity.\n
More than ‘Running Reports’. Not predictable like cows. Herding Cats.\nYou need to do it for your own needs.\n
Rare Opportunity. It’s coming.\nToo good for “I’m busy” or “Maybe Later”\n
So how do you do this?\nKeep steps separate.\n
Reading email, Oh-no, one of my favorite meds has a new warning.\nNone of the built-in reports get me pt’s taking the 80mg Dose.\n
Some problems you know you have the data, you just need to get it.\nLet’s Face It: not in 20 minutes. Takes time/persistence.\nSimple version, doable. \nThink simple list or flat file as output.\n
These are investments/hurdles.\nOvertime ROI improves\n
How to get my Simvastatin Data?\nConnecting to Database\n
Same details but in Crystal.\n
In general use Views where possible.\nFor Zocor: You need these 3.\n
\n
Joining is a key aspect of relational database.\n
\n
So obviously, it’s not this fast your first time!\nBut responding to an FDA announcement is actually doable even with a busy schedule.\n
If you can do SQL, maybe use the built-in reports.\nFor when you need just one more column\n
So we’ve got a list dumped out of the database.\nNow it’s time to do something with the data.\n
Continuity is a benchmark of quality care. Let’s say I can dump out a list of visits from a report. How do we dice that info into something?\n
Let Face it 2: Excel takes more than 20 minutes to master.\nI think of dicing the list as adding a bit more logic/interpretation to data.\n\n
Spreadsheets are more approachable than Crystal.\nSubset of functions can be combined in multiple ways.\n\n
Quick Look around.\n\n
Top ten ICDs or CPTs, etc.\n
Horizontal Analysis!\n
\n
\n
\n
\n
\n
So Dump a flat file list of data\nAnalyze horizontally with logic and lookups\nAnalyze vertically with a pivot table\nYou’re not done! You have to present it for ‘humans’\n
Needs Color, Organization.\nNot too many numbers.\nNot too few either.\n
Although you won’t master it now:\nDoable as a ‘hobby’.\n
\n
\n
Allow lots of data \n
\n
\n
\n
\n
\n
So what’s the problem with my 3 step process?\n
This is not a tech problem.\nEMR transition is like this.\n
Cognitive Biases: Mistakes you make at the beginning\nSystems Errors: Mistakes you make on follow through\n