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. 2009 Feb;6(2):492-525.
doi: 10.3390/ijerph6020492. Epub 2009 Feb 5.

Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking

Melanie Swan  1
Affiliations

Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking

Melanie Swan. Int J Environ Res Public Health. 2009 Feb.

Abstract

A new class of patient-driven health care services is emerging to supplement and extend traditional health care delivery models and empower patient self-care. Patient-driven health care can be characterized as having an increased level of information flow, transparency, customization, collaboration and patient choice and responsibility-taking, as well as quantitative, predictive and preventive aspects. The potential exists to both improve traditional health care systems and expand the concept of health care though new services. This paper examines three categories of novel health services: health social networks, consumer personalized medicine and quantified self-tracking.

Keywords: Internet; Patient-driven health care; health care delivery; health social networks; long-tail medicine; open source; personalized medicine; predictive health care; preventive health care; quantified self-tracking.

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Figures

Figure 1.
Figure 1.
A new model of health and health care.
Figure 2.
Figure 2.
Services provided by health social networks.
Figure 3.
Figure 3.
PatientsLikeMe detailed profile examples for an HIV, MS, Parkinson’s Disease and Anxiety patient (left) and DailyStrength activity feed items (right).
Figure 4.
Figure 4.
Example of a MedHelp physician homepage.
Figure 5.
Figure 5.
PatientsLikeMe ALS profile charts example.
Figure 6.
Figure 6.
CureTogether patient-entered treatment statistics.
Figure 7.
Figure 7.
PatientsLikeMe peer disease tracking over time with easy-to-read graphical images of patient status.
Figure 8.
Figure 8.
Carlson curves [15].
Figure 9.
Figure 9.
Excerpts from 23andMe’s condition risk profile for a sample patient.
Figure 10.
Figure 10.
Excerpts from 23andMe’s colorectal cancer marker for a sample patient.
Figure 11.
Figure 11.
Excerpts of raw genomic data from 23andMe, the environmental scan from the ExperimentalMan project and virtual patient predictive biosimulation output from Entelos.
Figure 12.
Figure 12.
Many Eyes data visualization of this paper.
Figure 13.
Figure 13.
Quantified self-tracking tools: FitBit, GoWearFit and Tweet what you eat!
Figure 14.
Figure 14.
Personal environmental monitoring tools: PEIR and Current Cost.
Figure 15.
Figure 15.
Examples of publicly-available patient reviews of physicians (Yelp).
See this image and copyright information in PMC

References

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MeSH terms