Regulatory & Landscape

Social Media, Internet of Things, and the Future of Public Health
Social Media, Internet of Things, and the Future of Public Health
  • Report ID: MCP-09092013
  • Published by: Mind Commerce Publishing

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Published: Sept, 2013 | Pages: 90

 

Public health is "the science and art of preventing disease, prolonging life and promoting health through the organized efforts and informed choices of society, organizations, public and private, communities and individuals." It is concerned with threats to health based on population health analysis. This definition highlights the role played by members of the community in improving health and in defining what is socially and publicly acceptable.

 

There are three main features of public health that define the field and also provide a contrast to the related field of medicine. Public health and medicine often have the similar goals of reducing the impact of disease and improving health and quality of life, but there are some notable differences between the two in the methods of reaching these goals. The primary features of public health are a (1) a view that all people should have healthcare access, regardless of social position, (2) a focus on the health of populations rather than individuals, and (3) a focus on prevention.

 

The use of social media in public health is taking shape as Twitter and other forms of social media are leveraged to identify potential outbreaks.  Public health data is rapidly increasing from all sources.  Sensors, formerly found only in hospital ICUs, are now portable and be used at home, and even sometimes while walking.  The potential for persistent public health monitoring may be realized through introduction of machine-to-machine (M2M) communications, embedded computing/wireless and related technologies.

 

This report evaluates the impact of social media, Big Data and analytics, and the so called "Internet of Things (IoT)" on public healthcare.  The report evaluates specific companies, solutions, and applications.  The report includes analysis of Big Data and its anticipated use in public health.

 

Report Benefits:

  • Identify the tenants of "Public Health 2.0"
  • Identify specific companies, solutions, and applications
  • Identify the role of Participatory Epidemiology in public health
  • Understand the confluence of Infodemiology and Infoveillance
  • Understand the role of data-mining, Big Data, and public health
  • Understand the role and importance of social media in public health
  • Understand the evolution and future direction of healthcare technology

 

Companies and Organizations in Report:

  • Abbott Laboratories
  • Adidas miCoach
  • Airstrip Technologies
  • Al Bawaba
  • AliveCor
  • Asthmapolis
  • AstraZeneca
  • BioCaster Global Health Monitor
  • Blue Shield of California
  • CardioNet
  • Centers for Disease Control and Prevention (CDC)
  • Centers for Medicare & Medicaid Services (CMS)
  • Central Intelligence Agency
  • DexCom
  • EpiSPIDER
  • Facebook Inc.
  • Factiva
  • Flu Detector
  • Food and Drug Administration (FDA)
  • FrontlineSMS
  • GeoChat
  • Geonames
  • Global Public Health Intelligence Network (GPHIN)
  • Google Dengue Trend
  • Google Flu Trends
  • Google Maps
  • HealthConnect
  • HealthCore
  • HealthMap
  • IMS Health
  • iRhythm
  • Kaiser Permanente
  • Life Watch
  • Medtronic Inc.
  • Microsoft Research
  • Mood of the Nation
  • NantHealth
  • National Cancer Institute (NCI)
  • National Institutes of Health (NIH)
  • Nike+
  • Philips Electronics iPill
  • Proteus Digital Health
  • Prudential Insurance Company of America
  • Sickweather
  • Skin Scan
  • Sotera Wireless
  • Twitter Inc.
  • U.S. Department of Health and Human Services
  • UK Health Protection Agency
  • United Nations
  • United States Public Health Service (PHS)
  • U.S. Department of Agriculture
  • U.S. Department of Homeland Security
  • U.S. Director for National Intelligence
  • Ushahidi
  • Vitality
  • Withings
  • World Health Organization (WHO)
  • Yahoo Maps

 

 

Target Audience:

  • Healthcare service providers
  • Healthcare insurance providers
  • Healthcare technology companies
  • Social media and networking companies
  • Policy makers, regulatory bodies, and government
  • Consultants and systems integrators for healthcare technology
  • M2M, Internet of Things (IoT), and general telecommunications companies
  • Public health institutions including CDC, NIH, FDA, CMS, NRHA, WHO, and others

 

 

Table of Contents:

 

1              Executive Summary  5

 

2              Introduction                7

 

3              History of Public Health        9

 

4              Public Health Practice            13

4.1          The U.S. Public Health System    14

 

4.2          Public Health at the Federal Level             14

4.2.1      Centers for Disease Control and Prevention (CDC)           15

4.2.2      National Institutes Of Health (NIH)           15

4.2.3      Food and Drug Administration (FDA)       15

4.2.4      Centers for Medicare & Medicaid Services (CMS)             16

 

4.3          Public Health at the State and Local Levels            16

 

5              Public Health Data    17

5.1          Surveillance        18

 

5.2          Secondary Data                19

5.2.1      The Census         19

5.2.2      Vital Statistics    20

5.2.3      Survey Data        22

5.2.4      Registries            24

5.2.5      Reporting Systems          24

5.2.6      Medicare and Medicaid                26

 

5.3          Studies 26

5.3.1      Observational Studies    28

5.3.2      Experimental Studies     30

5.3.3      Screening            32

 

5.4          Epidemiology     34

5.4.1      Basic Epidemiological Measures                35

 

6              Datamining Social Media, the Internet of Things And Big Data                38

6.1          Data Mining        38

6.1.1      Preprocessing   39

6.1.2      Data Mining and Analysis              40

6.1.3      Text Mining        41

 

6.2          The Internet of Things   42

 

6.3          Big Data                44

 

7              Social Media, Datamining, and Big Data in Public Health             47

7.1          Public Health 2.0               48

 

7.2          Twitter 49

7.2.1      Influenza and Food Poisoning In New York           50

7.2.2      Post-Partum Depression              52

7.2.3      H1n1 in Germany            53

7.2.4      Seasonal Allergies            54

 

7.3          Mining the Web               55

7.3.1      Datamining News Stories To Predict Cholera Outbreaks  55

7.3.2      Data Mining Downloaded Recipes to Study Dietary Habits             56

7.3.3      Datamining Online Web Searchs to Predict Influenza Outbreaks                57

7.3.4      Online Physician Ratings               58

 

7.4          Participatory Epidemiology          58

7.4.1      Frontlinesms     59

7.4.2      Ushahidi              59

7.4.3      Geochat              59

7.4.4      Asthmapolis       60

7.4.5      The Gphin Project          60

7.4.6      The Healthmap Project 62

7.4.7      Outbreaks Near Me        63

7.4.8      Biocaster             64

7.4.9      Sickweather       64

7.4.10    The Epispider Project   65

 

7.5          Reality Mining   66

 

7.6          Infodemiology And Infosurveillance        67

7.6.1      Google Flu Trends           69

7.6.2      Google Dengue Trend   69

7.6.3      Flu Detector       70

7.6.4      Mood of the Nation        70

 

7.7          Big Data and Public Health            71

7.7.1      Kaiser Permanente Healthconnect          72

7.7.2      Blue Shield of California And Nanthealth               73

7.7.3      Astrazeneca and Healthcore      73

 

8              The Internet Of Things And The Move To Individuals In Public Health  75

8.1          Population vs. Individual Thinking             75

 

8.2          Internet of Things            77

8.2.1      Diabetes              78

8.2.2      Ecg and Heart Monitoring           80

8.2.3      Vital Signs            81

8.2.4      Asthma                83

8.2.5      Medical Compliance       84

8.2.6      Smartphone Lab               85

 

9              Conclusions and Recommendations           87

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