Automated Personal Health Records Essential Interstitium

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Automated Personal Health RecordsEssential
Interstitium

• Isaac Zak Kohane, MD, PhD
• HMS Center for Biomedical Informatics

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Drivers

• Genetics Genome-scale Measurements
• Relative Underperformance of the Healthcare and
  Biomedical Research System

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Genetics As Destiny
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Prismatic Example PPARg Pro12Ala and diabetes
Oh et al.
Deeb et al.
Mancini et al.
Clement et al.
Hegele et al.
Hasstedt et al.
Lei et al.
Ringel et al.
Sample size
Hara et al.
Overall P value 2 x 10-7 Odds ratio 0.79
(0.72-0.86)
Meirhaeghe et al.
Douglas et al.
Altshuler et al.
Mori et al.
All studies
Estimated risk (Ala allele)
0.2
0.4
0.6
0.8
1
1.2
2.0
1.4
1.6
1.8
0.1
0.3
0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
Ala is protective
Courtesy J. Hirschhorn
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Compared to what?

• Relative Risk Rexp/Rcontrol

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Concerns of Identifiability

• 1010 humans living
• Unrelated humans 99.9 genetically alike
• How many SNPs to identify you?

Lin et al.
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Failure 1
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• Failure 2 No Life-long medical record
• Highly mobile patients
• No formalized data exchange

X
X
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A Fraught Solution 1994
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A Fraught Solution 2005
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Not the Solution
Tethered PHR
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Solution Instance PING

• Personal Internetworked Notary and Guardian
• Personally controlled records
• Ubiquitous
• Open standards
• Open source
• Web interface
• Public key infrastructure
• XML-based data

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PCHR-centric (Aristotelian)
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• Indiana screenshot and photo here.

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Our Watson, come here I need you moment
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But is this sufficient?

• Can we invert the now traditional relationship
  between patient and researcher?
• Do all countries worry as much about privacy?
• Do patients prefer to disclose to their doctor
  more than their family?
• Are all individuals equally comfortable with
  personal disclosure?

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Information Exhibitionism or Altruism?
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A 3-part Solution
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Legislation
Selectable Disclosure
Information Altruism
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Information Opportunim

• Who literally profits from patient data?
• 18K/annum per patient for doctors?
• Should patients?
• How can we reimagine the patient-research
  relationship?
• Turning liability and risk into opportunity

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Our patients and our biomedical research goals at
risk

• Jill Genomus is a genomic researcher studying
  Autism Spectrum Disorder (ASD). In a
  high-resolution genome-wide scan and
  transcriptome-wide profiling of the patients in
  her anonymized database, she has two particular
  findings that appear to require follow-up. The
  first is the incidental finding in two siblings
  in one family that reveal a segmental chromosomal
  duplication that puts them at very high risk for
  colon cancer. The second is a single nucleotide
  polymorphism that is present in five children
  affected with ASD and no others.
• Jill has a pang of anxiety when she realizes that
  she does not know who any of these individuals
  are and furthermore is proscribed by the
  Institutional Review Board (IRB) protocol that
  was approved for this study from re-contacting
  any study subject.

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The Informed Cohort
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Applications

• Gathering of genomically informed adverse events
  DURING phase III trials and into phase IV.
• Public Health alerts that are targeted only to
  individuals at genetic and environmental personal
  risk.
• Recruitment of patients without conflict of
  interest from recruiting physicians.

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The Electronic Medical Record will fail without
broad implementation of PCHR

• To make it feasible to achieve large N
• To make the EHR truly lifelong
• To harness patients activation as consumers
• To overcome privacy concerns and leverage
  volunteerism
• To overcome stark knowledge and time deficits in
  the interaction between patients and healthcare
  system.

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Welcome to our brave new world!

• Thank you.

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What does this alarming calculation presume?
The constitutionality of DNA sampling on
arrest. Cornell Journal of Law and Public Policy,
June, 2001 by Kaye, D.H. INTRODUCTION Every
state now collects DNA from people convicted of
certain offenses. (1) Law enforcement authorities
promote offender DNA databanking on the theory
that it will clear previously unsolved crimes and
identify offenders who commit additional crimes
while on probation or parole, or after they have
finished serving their sentences. Even relatively
small databases have yielded such dividends. (2)
As these database searches uncover the
perpetrators of rapes, murders, and other
offenses, the pressure builds to expand the
coverage of the databases. (3) As the databanks ..
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Peer Pressure
The men of Wee Waa