Evidence Farming and Open Architecture

•

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This document discusses evidence farming as a new approach to evaluating mobile health (mHealth) applications. It outlines limitations of current evaluation methods like randomized controlled trials and data mining of electronic health records. It proposes an "evidence macrosystem" using open architectures to support evidence extraction through approaches like rooting for evidence, industrial evidence farming, personal evidence gardening, and crowdsourcing what matters to patients. The goal is a learning community that enables broad, rapid dissemination of evaluation methods and findings through shared modules and libraries within mHealth applications.

Technology Health & Medicine

Evidence
Farming1:
Implications
for
Open
Architecture
Ida
Sim,
MD,
PhD
Director,
Center
for
Clinical
and
Translational
Informatics
University
of
California
San
Francisco
May
5,
2011

1With
thanks
to
Rich
Kravitz
MD,
UC
Davis
and
Naihua
Duan,
Columbia

Rephrasing
“Does
it
Work?”

(Complexes of)
Outcome
Exposures strength of association? Increased
Text4Baby individual breastfeeding

population

Current
Approaches:
RCT
Asthma App ER visits at 1 year
50 people
100 people
Usual Care ER visits at 1 year
50 people population

• Tests
prespeciﬁed
interventions
and
outcomes
• To
conﬁrm
a
hypothesis
at
the
population
level
• Strong
internal
validity
• Problems:
slow
to
set-‐up,
expensive,
short-‐term,
lack
relevance
to
the
real
world

Current
Approaches:
Data
Mining

EHR
Exposures Outcomes
?
Apps
population

• Exposures
and
outcomes
from
care
process
systems
• To
generate
hypotheses
at
the
population
level
• Problems:
limited
to
data
collected,
weak
internal
validity
(data
not
complete
or
systematic)

Current
Approaches:
N-‐of-‐1
Studies
Asthma app Usual Care Asthma app
peak flow peak flow
Usual Care Asthma app Usual Care
individual

• Within-‐subject
multiple
crossover
• Only
formal
method
for
determining
individual
treatment
eﬀectiveness
• Problems:
complicated
to
set
up,
analysis
is
diﬃcult,
little
known,
not
widely
used

Evidence
Extraction

• Evidence
is
something
to
be
extracted
from
the
care
process
– mining
it
from
the
data
– directly
manipulating
the
care
process
with
rigid
and
pre-‐deﬁned
protocols

Evidence
Farming

Hay, et al. J Eval Clin Prac 14(2008):707-713.

Industrial
Evidence
Farming
Asthma App ER visits at 1 year
50 people
100 people
Usual Care ER visits at 1 year
50 people population

Personal
Evidence
Gardens
Asthma app Usual Care Asthma app
peak flow peak flow
Usual Care Asthma app Usual Care
individual

Personal
Evidence
Gardens
Flovent Flovent PRN Flovent
dancing dancing
Flovent PRN Flovent Flovent PRN
individual

Crowdsourcing
What
Matters
• (Complexes
of)
Exposures
– does
chocolate
trigger
(my)
asthma?
– testing
common
regimens
(ACEI,
statin,
b-‐blocker),
complementary
medicines
• (Complexes
of)
Outcomes
– what
outcomes
do
patients
care
about?

Evidence
Macrosystem
Rooting for Industrial Evidence Personal Evidence
Evidence Farming Gardens

Stovepiped
mHealth
• Health
apps
built
independently
– little
data
sharing
and
interoperability
• Limits
eﬃciency
and
impact
of
quality
mHealth

Internet
Hourglass
Model
• Standardize
and
make
open
the
“narrow
waist”
• Reduces
duplication,
spurs
community
innovation,
supports
commercial
and
non-‐
proﬁt
uses

OpenmHealth.org

Estrin DE, Sim I. Science; 330: 759-60. 2010.

OpenmHealth.org

• The
waist
should
support
the
evidence
macrosystem

Open
Architecture
for
an
Evidence
Macrosystem
• Modules
for
usage
analytics
– #
of
text
messages,
#
of
sessions,
etc.
• Rooting
for
(glocal)
evidence
– data
sharing
with
shared
syntax
and
semantics
• Industrial
farming,
e.g.,
with
RCTs
– modules
for
informed
consent,
randomization,
adaptive
treatment
strategy,
mixed
methods,
etc.
• Personal
evidence
gardening,
e.g.,
N-‐of-‐1
– modules
for
scripting
and
analyzing
individualized
N-‐of-‐
1
protocols,
etc.

Open
Architecture
for
an
Evidence
Macrosystem
• Social
media
for
discovery
of
exposures
and
outcomes
that
matter
• Shared
libraries
of
validated
measures
and
instruments
(e.g.,
PROMIS)
– measures
that
get
at
ﬁner-‐grained
mechanisms
based
on
theoretical
models
of
change,
etc.

Goal
for
mHealth
Evidence
• A
learning
community
coupled
with
an
open
architecture
for
broad,
rapid,
and
iterative
dissemination
of
evaluation
methods
and
ﬁndings
that
matter

• Ida
Sim
ida.sim@ucsf.edu
• Deborah
Estrin
destrin@cs.ucla.edu
• http://openmhealth.org/

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11 am sim

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The document discusses evidence farming as an approach to evaluating mobile health (mHealth) applications and interventions. Evidence farming involves extracting evidence from care process data and manipulating care processes with flexible protocols. This allows generating hypotheses at the population level while maintaining some internal validity. The document proposes an open architecture called OpenmHealth to support evidence farming and crowdsourcing evidence through modules for usage analytics, randomized trials, individualized studies, and sharing findings. The goal is a learning community that can rapidly disseminate and iterate on evaluation methods that matter to improving mHealth.

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This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).

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Evidence Farming and Open Architecture

1. Evidence Farming1: Implications for Open Architecture Ida Sim, MD, PhD Director, Center for Clinical and Translational Informatics University of California San Francisco May 5, 2011 1With thanks to Rich Kravitz MD, UC Davis and Naihua Duan, Columbia

2. Rephrasing “Does it Work?” (Complexes of) Outcome Exposures strength of association? Increased Text4Baby individual breastfeeding population

3. Current Approaches: RCT Asthma App ER visits at 1 year 50 people 100 people Usual Care ER visits at 1 year 50 people population • Tests prespeciﬁed interventions and outcomes • To conﬁrm a hypothesis at the population level • Strong internal validity • Problems: slow to set-‐up, expensive, short-‐term, lack relevance to the real world

4. Current Approaches: Data Mining EHR Exposures Outcomes ? Apps population • Exposures and outcomes from care process systems • To generate hypotheses at the population level • Problems: limited to data collected, weak internal validity (data not complete or systematic)

5. Current Approaches: N-‐of-‐1 Studies Asthma app Usual Care Asthma app peak flow peak flow Usual Care Asthma app Usual Care individual • Within-‐subject multiple crossover • Only formal method for determining individual treatment eﬀectiveness • Problems: complicated to set up, analysis is diﬃcult, little known, not widely used

6. Evidence Extraction • Evidence is something to be extracted from the care process – mining it from the data – directly manipulating the care process with rigid and pre-‐deﬁned protocols

7. Evidence Strip Mining

8. Evidence Farming Hay, et al. J Eval Clin Prac 14(2008):707-713.

9. Rooting for Evidence

10. Industrial Evidence Farming Asthma App ER visits at 1 year 50 people 100 people Usual Care ER visits at 1 year 50 people population

11. Personal Evidence Gardens Asthma app Usual Care Asthma app peak flow peak flow Usual Care Asthma app Usual Care individual

12. Personal Evidence Gardens Flovent Flovent PRN Flovent dancing dancing Flovent PRN Flovent Flovent PRN individual

13. Crowdsourcing What Matters • (Complexes of) Exposures – does chocolate trigger (my) asthma? – testing common regimens (ACEI, statin, b-‐blocker), complementary medicines • (Complexes of) Outcomes – what outcomes do patients care about?

14. Evidence Macrosystem Rooting for Industrial Evidence Personal Evidence Evidence Farming Gardens

15. How can we scale evaluation?

16. Stovepiped mHealth • Health apps built independently – little data sharing and interoperability • Limits eﬃciency and impact of quality mHealth

17. Internet Hourglass Model • Standardize and make open the “narrow waist” • Reduces duplication, spurs community innovation, supports commercial and non-‐ proﬁt uses

18. OpenmHealth.org Estrin DE, Sim I. Science; 330: 759-60. 2010.

19. OpenmHealth.org • The waist should support the evidence macrosystem

20. Open Architecture for an Evidence Macrosystem • Modules for usage analytics – # of text messages, # of sessions, etc. • Rooting for (glocal) evidence – data sharing with shared syntax and semantics • Industrial farming, e.g., with RCTs – modules for informed consent, randomization, adaptive treatment strategy, mixed methods, etc. • Personal evidence gardening, e.g., N-‐of-‐1 – modules for scripting and analyzing individualized N-‐of-‐ 1 protocols, etc.

21. Open Architecture for an Evidence Macrosystem • Social media for discovery of exposures and outcomes that matter • Shared libraries of validated measures and instruments (e.g., PROMIS) – measures that get at ﬁner-‐grained mechanisms based on theoretical models of change, etc.

22.

23. Goal for mHealth Evidence • A learning community coupled with an open architecture for broad, rapid, and iterative dissemination of evaluation methods and ﬁndings that matter

24. • Ida Sim ida.sim@ucsf.edu • Deborah Estrin destrin@cs.ucla.edu • http://openmhealth.org/

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