Transcript Guidelines, Personalised Healthcare and Real World Data

Martin S. Kohn, MD, MS, FACEP, FACPE
Chief Medical Scientist, Sentrian
Real World Data,
Guidelines and
Decision Support
1
Limitations of Traditional Guidelines
• Consensus documents rather than truly
evidence based
• Data usually from published trials
• Inherently short term
• Artificial environment
• Population level comparisons
• Focused on one disease state
• Null hypothesis constraint
• May be out of date by the time they are
published; not easily amended
Published Literature
It is simply no longer possible to believe much of the
clinical research that is published, or to rely on the
judgment of trusted physicians or authoritative
medical guidelines.
Marcia Angell, former editor-in-chief, NEJM
Drug Companies & Doctors: A Story of Corruption NY Review of Books Jan 15, 2009
“A lot of what is published is incorrect.” ... much of
the scientific literature, perhaps half, may simply be
untrue.
Richard Horton, Editor-in-Chief The Lancet
www.thelancet.com Vol 385 April 11, 2015
Published Data - John Ioannidis
“Much of what medical researchers conclude
in their studies is misleading, exaggerated, or
flat-out wrong.”
http://www.theatlantic.com/magazine/archive/2010/11/lies-damned-liesand-medical-science/308269/, accessed 3/11/2014
“There is increasing concern that in modern
research, false findings may be the majority or
even the vast majority of published research
claims.”
PLoS Med. Aug 2005; 2(8): e124. Published online Aug 30, 2005.
doi: 10.1371/journal.pmed.0020124
Pre-Clinical Pharma Research
Scientific findings were confirmed in only 6 of 53
(11%) “landmark” papers.
Begley CB, Ellis LM. Raise Standard for Preclinical Cancer Research. Nature 29 Mar
2012 483:531-533
…general impression that many results that are
published are hard to reproduce.
…only in ~20–25% of the projects were the
relevant published data completely in line with
our in-house findings
Prinz F, Schlange T, Asadullah K.NATURE REVIEWS | DRUG DISCOVERY 2011
Triple Blinding
One investigator — or, more typically, a suitable
computer program — methodically perturbs data
values, data labels or both, often with several
alternative versions of perturbation. The rest of the
team then conducts as much analysis as possible
‘in the dark’. Before unblinding, investigators
should agree that they are sufficiently confident of
their analysis to publish whatever the result turns
out to be, without further rounds of debugging or
rethinking.
MacCoun R, Perlmutter S. Hide results to seek the truth. Nature Oct 8,
2015
July 4,
1968
Big Data and Learning Health Systems
New Data Sources
New Thinking
• Inductive Reasoning
• Detecting Patterns
Learn from Daily Experience
Many Dimensions of Data
The Way Massive Data Sets are Used
Krumholz HM. Big Data and New Knowledge in Medicine: The Thinking, Training and Tools Needed for a Learning
Health System. Health Affairs. 33:7:1163-1170 July 2014
Uncertainty
IBM Global Technology Outlook 2012
Future of Clinical Decision Support
We will have massive data streams resulting
from pervasive monitoring and interactions with
personal health monitors, the environment, and
related public health data…the genome,
metabolome, proteome, and microbiome. This
implies the very nature of knowledge, and
reasoning or decision-making, are changing
under our feet.
Middleton et al. Clinical Decision Support: a 25 Year
Retrospective and a 25 Year Vision. IMIA Yearbook of
Medical Informatics 2016
Cycle of evidence in rapid-learning health care
Abernethy A, et al. J Clin Oncol.
2010;28:4268-4274
The algorithms of machine learning, which can
sift through vast numbers of variables looking
for combinations that reliably predict outcomes,
will improve prognosis, displace much of the
work of radiologists and anatomical
pathologists, and improve diagnostic accuracy
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine.
Ziad Obermeyer, M.D., and Ezekiel J. Emanuel, M.D., Ph.D.
N Engl J Med 2016; 375:1216-1219September 29, 2016DOI:
10.1056/NEJMp1606181
“For an idea
that does not at
first seem
insane, there is
no hope.”
Albert Einstein
Questions?
[email protected]
[email protected]