The Doctor Social Graph Project, hosted on MedStartr (healthcare version of KickStartr) is a healthcare data project opening up Physician referral data and much more. http://thehealthcareblog.com/blog/2012/11/05/tracking-the-social-doctor-opening-up-physician-referral-data-and-much-more/
Follow our presentation to learn about the role of statistical analysis in fraud detection. From data mining to clustering, learn the techniques necessary to quickly anticipate and detect health care fraud, waste, and abuse.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
Follow our presentation to learn about the role of statistical analysis in fraud detection. From data mining to clustering, learn the techniques necessary to quickly anticipate and detect health care fraud, waste, and abuse.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
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Healthcare data is hard to deal with and getting even harder and more expensive. In this presentation, Shahid Shah covers why:
* Healthcare data is going from hard to nearly impossible to manage.
* Applications come and go, data lives forever.
* Data integration is notoriously difficult, even in the best of circumstances, and requires sophisticated tools and attention to detail.
And, then talks about how new techniques are needed to store and manage healthcare data.
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This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
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Splunk’s data analytics platform could be utilized to solve many high impact business problems in healthcare delivery systems to reduce cost, improve patient outcome and safety, and enhance care coordination experience. Analyze observed behavior from healthcare event data and metadata to discover patterns, monitor compliance, and optimize the workflow. Furthermore 80% of healthcare data is unstructured (clinical free text and documentation), or semi-structured and many new data sources are such as tele health, mobile health, sensors, and devices are getting integrated in many healthcare systems specifically in the area of chronic disease management. So, one need analytics software that can harvest, interpret, enrich, normalize, and model diverse structured and unstructured data and analytics approaches that embrace the “data turmoil” by relying less on standardized data items and more on the capability to process data in any format.
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About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
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This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
Revenue opportunities in the management of healthcare data delugeShahid Shah
Healthcare data is hard to deal with and getting even harder and more expensive. In this presentation, Shahid Shah covers why:
* Healthcare data is going from hard to nearly impossible to manage.
* Applications come and go, data lives forever.
* Data integration is notoriously difficult, even in the best of circumstances, and requires sophisticated tools and attention to detail.
And, then talks about how new techniques are needed to store and manage healthcare data.
BigInsight seminar on Practical Privacy-Preserving Distributed Statistical Co...Statistisk sentralbyrå
Seminar Monday March 5th 2018 by BigInsight and Statistics Norway: Presentation by Kassaye Yitbarek Yigzaw. Distributed data analysis in the face og privacy concerns.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
Blockchain Life Science Opportunity Traumatic Brain InjurySean Manion PhD
This is a presentation on the opportunities to use blockhain/distributed ledger technology to improve federal traumatic brain injury (TBI) research. This presentation was given by Dr. Sean Manion, CEO of Science Distributed and former federal research administrator with the Defense & Veterans Brain Injury Center (DVBIC) at the 03 May Blockchain in Healthcare Summit hosted by MATTER and UIC in Chicago along with sponsors SAP and Advanced Clinical.
Big Data Analytics for Healthcare Decision Support- Operational and ClinicalAdrish Sannyasi
Splunk’s data analytics platform could be utilized to solve many high impact business problems in healthcare delivery systems to reduce cost, improve patient outcome and safety, and enhance care coordination experience. Analyze observed behavior from healthcare event data and metadata to discover patterns, monitor compliance, and optimize the workflow. Furthermore 80% of healthcare data is unstructured (clinical free text and documentation), or semi-structured and many new data sources are such as tele health, mobile health, sensors, and devices are getting integrated in many healthcare systems specifically in the area of chronic disease management. So, one need analytics software that can harvest, interpret, enrich, normalize, and model diverse structured and unstructured data and analytics approaches that embrace the “data turmoil” by relying less on standardized data items and more on the capability to process data in any format.
Data Governance Talking Points: Simple Lessons From the TrenchesHealth Catalyst
About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
The Distributed Science Ecosystem, Sean Manion TFECON 2018Sean Manion PhD
"The Distributed Science Ecosystem," is a presentation from Sean Manion, PhD of Science Distributed to the Johns Hopkins Technology & Future Economy 2018 Conference on 07 Apr 2018 looking at the application of blockchain to health science research from a business perspective.
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Comprehensive coverage of fundamentals of computer graphics.
3D Transformations
Reflections
3D Display methods
3D Object Representation
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Abigail Sears, Chief Executive Officer, OCHIN
Sharon Wentz, RN, Business Development Coordinator, CareAccord
Laurie Miller, RHIT, CCS-P, HISP Administrator, Gorge Health Connect
Paula Weldon, Project Manager, Jefferson Health Information Exchange
Big data is more than just a buzzword in healthcare. It's the promise of being able to extract, cull, and interpret medical data to directly benefit population and individual health. learn more about the benefits of big data, roadblocks to leveraging it's potential, how Meaningful Use enablesbig data, what types of cross-country collaboration projects are advancing the use of big data on an international scale, big data's impact on patient privacy and much more! Special thanks to Mandi Bishop for her time on the podcast.
Presentation by Megan Douglas, JD for the Third Annual Policy Prescriptions® Symposium
She is the associate director of Health Information Technology Policy in the National Center for Primary Care at Morehouse School of Medicine.
The symposium is designed for clinicians, healthcare workers, and healthcare executives interested in exploring the major themes that will emerge in health policy throughout the year. This year, the symposium will emphasize value in healthcare, health information technology, gun violence, insurance choices, the Affordable Care Act, and the viewpoints of the Presidential candidates on health care.
For more information contact: Slideshare@marcusevans.com
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In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
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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
Integrating Ayurveda into Parkinson’s Management: A Holistic ApproachAyurveda ForAll
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
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ASA GUIDELINE
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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
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
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.
ABDOMINAL TRAUMA in pediatrics part one.drhasanrajab
Abdominal trauma in pediatrics refers to injuries or damage to the abdominal organs in children. It can occur due to various causes such as falls, motor vehicle accidents, sports-related injuries, and physical abuse. Children are more vulnerable to abdominal trauma due to their unique anatomical and physiological characteristics. Signs and symptoms include abdominal pain, tenderness, distension, vomiting, and signs of shock. Diagnosis involves physical examination, imaging studies, and laboratory tests. Management depends on the severity and may involve conservative treatment or surgical intervention. Prevention is crucial in reducing the incidence of abdominal trauma in children.
1. Introduction to the Doctor
Social Graph project
Brandon Weinberg : November 29, 2012
This presentation is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
2. Before I Start...
● As the Doctor Social Graph project rapidly
progresses, obsolence will kick in rendering
this content stale and "old news"
● This presentation was published on
Slideshare 11/29/2012 when the Doctor
Social Graph Project was quite new
● Details as of 11/29 are gradually emerging;
Most content in these slides is paraphrased
from official project announcements thus far
● Let's get started!
3. Organizer
● Fred Trotter
● Celebrated Health IT Expert in USA
● One of the Designees of the Direct Project
(Mandated HIE Protocol in USA)
● Co-Authored First Health IT Book for O'Reilly
and Most Popular Book on Meaningful Use
Standards: Meaningful Use And Beyond
● Values Open Source
4. Announcement
● Strata RX 2012- O'Relly Strata Conference
● October 16, 2012
● San Francisco
● Fred's Keynote Titled "The Ethos of
Healthcare Data Science"
● This Was When Data Was Initially Released
(Open Source Licensed), For Healthcare
Data Scientists in Audience
5. Social Dataset
● Collaborative Relationship Data
● How Doctors, Hospitals, Labs and Other
Healthcare Providers Collaborate To Treat
Medicare Patients
● Data Includes: Referrals to Specialists
● Data Includes: Lab Providers and Hospitals
A Doctor Often Works With
● Data Includes: Real Names and Addresses
● Representative of How USA Healthcare
System is Delivering Care
6. Doctor Social Graphs
● Graphical Representations of Group
Interactions During Medicare Treatment
● Diagrams Based on Math Models
● Use Nodes and Connections
● Nodes: Providers, e.g. Doctors, Hospitals,
Labs, Etc
● Connections: Degree to Which Providers
Work Together Treating Specific Patients
● Will Be Largest Real-Name Social Graph
That is Publically Available, Of Any Kind!
7. Doctor Social Graphs
● Visualization of Social Graph begins at 1:10
● http://youtu.be/L4C3cloZEQk
8. Other Social Graphs
● Facebook, Twitter, LinkedIn Exemplify
Private Big Data Social Graphs
● Most Portions of Data Remain In-House
● Do You Know Any Data Scientists Good at
Graphing and Graph Theory? They May
Appreciate Doctor Social Graph
9. Preparing Data
● Initial Dataset Was Obtained by Fred Trotter
● He Filed A Freedom of Information Act
Request Against Medicare Claims Database
● For Phase 1 Improvement, He Purchased
Board Credentialing Data in All 50 States
● Was $50-$1,000 Per State to Download
● Board Credentialing Data is Analogous to
"Credit History" for Doctors. e.g. Medical
Schools, Board Certifications and Board-
Imposed Punishments
10. Preparing Data
● After Merging Initial (Referral and Teaming)
Dataset with State Credentialing Data, the
Data Was Formatted For Usability, e.g.
Disparate Data Sources Will Be Formatted in
CSV, JSON, XML
● Merged Dataset To Be Released in Late
November or Early December to MedStartr
Backers (Explained Later)
11. Doctor Performance
● Fairly Evaluate Doctor Quality in USA
● "My Most Important Project For This Data Is
Simple: I Want To Create Algorithms To
Rate Doctors That Patients Find Useful And
That Doctors Find Fair." Fred Trotter
(paragraph 10)
● "The Development of Objective, Fair and
Useful Doctor Rating Systems" Fred Trotter
12. Doctor Performance
● Referrals From Doctors, For Example, May
Be Used As Doctor "Votes" For Each Other
● Scroll Down to Third Paragraph Why This
Matters To Patients For Challenges and
Biases in Current Doctor Rating Systems
● Examples Abound How Patients, Doctors,
Insurance Companies, Hospitals, Labs,
Academics, Scientists, Health Policy Makers
and Others May Leverage Data For Their
Particular Research Interests
13. Hospital Performance
● Hospital Performance Data Sources Will Be
Merged and Improve Dataset
● e.g. Phase 3
● Example Question: Which Cardiologists
Refer to Hospitals With Poor Central Line
Infection Rates?
● "We Want to Turn This Into the Ultimate
Source For Open Doctor and Hospital Data."
Fred Trotter
14. Overview of Data
● 2011 Dataset is 1.3 GB file
● 3.7 Million Entries
● Contains Nearly One Million Nodes
● Node = Person or Organization That
Provided Health Care Service to a Medicare
Patient
● Graph Data is Keyed Using National
Provider Identifiers (NPIs)
15. NPI
● NPI = Unique Provider Number
● Individual and Organization Providers
● NPI is Mandated by HIPPA (as a
Replacement to UPIN)
● Doctors and Hospitals Must Use Their NPI
for Medical Billing, e.g. Medicare Billing or
Prescribing Medication
16. Sample Data
● A few lines from a random search (grep) on
a specific NPI...
grep 1548387418 refer.2011.csv >
Methodist_Hospital_Referrals.csv
NPI_Seen_First,NPI_Seen_Second,Seen_Count
1184710477,1548387418,55
1548387418,1326047754,62
1548387418,1598971913,24
● Pretty Cool, Huh? Full Sample is on
Pastebin
17. Tip For Providers
● Are You A Health Care Provider?
● Good Time To Update Your NPI Record
● e.g. No Need to List Your Home Address
● Public Database
● Updated Weekly
● Fred Built a Very Clean NPI Search Tool
● Or Use Government NPI Search Tool
18. Referral and Teaming
● Graph Has 49,685,586 Referring Party Pairs
(Collaborative Relationships)
● When Providers Work On The Same Group
of Patients Within The Same Time Frame =
Teaming Relationship
● Interactions Traditionally Considered
Referral Relationships = Majority of Data
● If Provider A Sees the Same Patient As
Provider B Within 30 Days, It Counts As +1
19. Referral and Teaming
● What's Counted is How Often Two Providers
Bill Medicare For The Same Patients in 30
Days
● How Can Patient Identification Be Avoided,
You May Ask
● For Each Entry in Dataset, At Least 11
Patients Were Involved in Transaction
● 11 = CMS Standard
● 11 Solves "Elvis Problem"
20. Elvis Problem
● Everyone Knows Elvis' Doctor
● Everyone Knows Elvis Doctor Has One
Patient
● If Elvis' Doctor Refers to a Cardiologist, Then
Everyone Knows Elvis Has Heart Problems
● At Least 11 Patients Take Part In Each
Given "Referral Count"
● Enforcing a Minimum of 11 Patients in the
Transaction Addresses Said Problem
21. Privacy
● Aside From Knowing a Score Reflects 11 or
More Patients, Little Else Can Be Derived
From Relationship Scores About Patients
● e.g. Referral Relationship Score = 1,100
● You Know it Reflects 11 or More Patients
● Was It 11 Patients With 100 Referrals?
● Was It 100 Patients With 11 Referrals?
● Bottom Line. Data Reflects the Relationship
Score Between Two Nodes, While Omitting
Patient-Specific Data
22. Privacy
● No Patient-Specific Data is Released in
Dataset; Patient-Specific Data is Entirely
Omitted (Not Deidentified)
● Doctors Who Bill Medicare Are Government
Contractors; Some Will Be Surpised As
Public Data Becomes Increasingly
Accessible
● Freedom of Information Act Makes
Government Contractor Data Available to
Public for Accountability
23. Privacy
● It is Fair to Presume Organizations Are
Already Using Such Healthcare Data
● e.g. Insurance Companies, Pharmacy
Chains, Government, Etc
● Ironically, Patients and Doctors Have Had
Least Access To Study Such Data
24. Data Overlay
● Information Will Be Discoverable By
Overlaying Private or Public Data On Top of
the Dataset
● Dataset With Medicare Referral and
Teaming Patterns Was a Starting Point to
Merge Data
● Dataset Will Be Steadily Improved
● In Phase 2, For Example, Publically
Available Nursing Home Data To Be Merged
25. Geo-Encoded
● Each Provider Identifier Contains Practice
Location Address and Mailing Address
● Data Can Be Overlayed Geographically and
Merged With Geo Databases
● Graph Gets Input From a Geo-Encoded Key
● 80%: Specific Latitude or Longitude
● 20%: Zip Code for General Location
● Localized Healthcare Data
26. Sample Data, Re-Examined
● 1112223334,5556667778,1111
● 1112223334 = NPI of Node That Saw
Medicare Patient First
● 5556667778 = NPI of Node That Saw
Medicare Patient Second
● 1111 = Number of Times This Happened in
a 30-Day Period During A Year (Connection)
● 1111 = Relationship Score Between Real-
Named Nodes and Connections
● Often (Not Always) the PCP = First Variable
27. Most Popular Referrals
● Fred Uploaded the Top 100 Organizations
by Number of Nodes in Dataset to Pastebin
● One of Most Frequent "Referrals" is to Get
Lab Work Done at LabCorp, Quest or Other
Local Lab Providers
● Also Very Common "Referrals" are to
Hospital Emergency Departments and
Treatment Facilities Like DaVita
28. Taxonomy
● Public NPI File Has Provider-Type Ontology
Classifying Doctor and Organization Types
● Hospitals, Primary Care Doctors, Specialist
Types and Labs are Coded in NPI File in
This Provider-Type Ontology; Which is
Maintained by AMA's National Uniform Claim
Committee
● Not Perfect, But Usually Accurate
29. Funding Overview
● Funding is Occasionally Needed to Improve
Dataset and Fred Uses Crowdfunding Model
● Project is Currently Hosted on MedStartr
(Healthcare Version of KickStarter)
● Backers Can Receive Early Access (6
Months) to a Rich Healthcare Dataset
● Entire Dataset Will Become Open Sourced
in Mid-2013 and Free to the Public
● License To Be Creative Commons
Attribution-ShareAlike 3.0 Unported License
30. Funding Overview
● MedStartr Backers Have Bought 1 of 2 Data
Licenses
● Open Source Data License
● $100-$120: Access to Entire Database and
Sharing of Any Integrated Data Required
● Proprietary-Friendly Data License
● $1,200-$5,000: Access to Entire Database
and Sharing of Integrated Data Not Required
31. Funding Details
● For Phase 1 Improvements to the Initial
Dataset $23,720 Collected From 88
MedStartr Backers; 51 Receive Data
● 39 Get Open Source Data License and 12
Get Proprietary-Friendly Data License
● Data Price Rises Per Phase Between Now
and Mid-2013 (Until Data Becomes Free)
● Dual-License = No Data Hoarding; Lets
Organizations Pay Steep Price to Innovate in
Private, Without Blocking Open Research
32. Crowdfunding
● Fred Effectively Said, "If A Few Hundred
People Want To Pool Small Amounts of
Money Together For This Project, I'll Buy
and Prepare Public-Yet-Inaccessible
Healthcare Data So Scientists Can Use It To
Improve Healthcare, and It Will Never Be
Hoarded."
● Clinical Trial Fundraiser Diabetes App
● Extend Features Patient Relationship App
● Not-Just-For-Profits: Transparent Funding
33. Call To Innovators
● "All of The Cool Discoveries in This Dataset
Should Happen in the First Six Months."
Fred Trotter
● "All of the Really Amazing Discoveries in
This Dataset Will Be Made in the Next Few
Months, By Those Who Either Attended
Strata RX, or Who Participate in This
Project." Fred Trotter
● Phase 2 Underway on MedStartr
34. Conclusion
● This presentation was made for people
learning about the Doctor Social Graph
project
● I hope it provides them a few things which
make understanding the project and data
easier and faster
● Have fun using the Doctor Social Graph
● Questions/Comments: Brandon Weinberg
● Email: b@brandonweinberg.com