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Perspective
The
NEW ENGLA ND JOURNA L
of
MEDICIN E
july 5, 2012
n engl j med 367;1 nejm.org july 5, 2012 1
outpatient visits. That care model
falls short not just because it is
expensive and often fails to pro-
actively improve health, but also
because so much of health is ex-
plained by individual behaviors,
1
most of which occur outside
health care encounters. Indeed,
even patients with chronic illness
might spend only a few hours a
year with a doctor or nurse, but
they spend 5000 waking hours
each year engaged in everything
else — including deciding wheth-
er to take prescribed medications
or follow other medical advice, de-
ciding what to eat and drink and
whether to smoke, and making
other choices about activities that
can profoundly affect their health.
The increasing attention being
paid to those 5000 hours takes
various forms. Employers are fo-
cusing more on employees’ well-
ness — how they eat, whether
they smoke, and how much they
exercise. Medication adherence has
become a more important goal,
thanks to growing recognition
that many people with chronic
conditions fail to take their medi-
cations regularly and therefore do
not get the benefits that health
care can provide. Home-based bio-
metric assessments of indicators
such as glucose level, blood pres-
sure, and weight are emerging as
part of longitudinal clinical care.
Transitional care models are be-
ing touted as a way of coordinat-
ing care beyond hospitalization.
And hospitals and health plans
are developing “hot-spotter” ap-
proaches, deploying tailored and
intensive attention to managing
the care of their most challeng-
ing patients.
2
All these activities
occur outside the conventional,
billable, clinical encounter — and
all reflect some sort of hovering
over people in their daily lives.
Conventional approaches to im-
proving patient engagement along
these dimensions have been per-
sonnel-intensive — using visiting
nurses or clinically staffed tele-
medicine services. Although re-
sults have been mixed, in general
these programs have not fulfilled
their promise. One problem is
that using personnel in hovering
is expensive and therefore diffi-
cult to scale up and to justify, ex-
cept for the very sickest patients,
some of whom might be too sick
to benefit. Another problem is
Automated Hovering in Health Care — Watching
Over the 5000 Hours
David A. Asch, M.D., M.B.A., Ralph W. Muller, M.A., and Kevin G. Volpp, M.D., Ph.D.
The dominant form of health care financing in
the United States supports a reactive, visit-
based model in which patients are seen when they
become ill, typically during hospitalizations and at
The New England Journal of Medicine
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Copyright © 2012 Massachusetts Medical Society. All rights reserved.
PERSPECTIVE
n engl j med 367;1 nejm.org july 5, 2012
2
that initiating and maintaining
patient engagement is difficult.
A large multicenter trial of tele-
monitoring for patients with heart
failure showed no effect on the
primary outcomes of rehospital-
ization and death; moreover, 14%
of those assigned to the interven-
tion group would not use the
system at all, and nearly half of
those who did lost interest over
time. One challenge, therefore, is
finding a way to automate hover-
ing to reduce its cost. A related
challenge is figuring out how to
incorporate it into people’s lives
in ways that are not just accept-
able and convenient, but ideally
even welcomed.
Three recent developments sug-
gest that automated hovering may
offer promise. First are early ef-
forts at payment mechanisms that
support more accountability for
health outcomes — including
nonreimbursement for prevent-
able readmissions and bundling of
payments around the goals of care
rather than encounters. These
changes provide a financial en-
gine to support automated hover-
ing initiatives.
The second development is
our deepening understanding of
behavioral economics and the re-
ality that although most people
want better health and typically
know what it would take to
achieve it, the desires, distractions,
and urgencies of the moment of-
ten get in the way of pursuing
what’s in their own long-term
self-interest. Behavioral econom-
ics explains why people are pre-
dictably irrational and provides
tools for redirecting their behav-
ior with carefully deployed nudg-
es and financial incentives.
3
The third development is the
expanded reach of both sophisti-
cated and simple technologies —
cell phones, wireless devices, and
the Internet — that can help
health experts connect to people
during their everyday lives. Nei-
ther wireless devices nor behav-
ioral economics were part of the
disease-management programs
that have produced mixed results
in the past.
There is already considerable
evidence of the promise of auto-
mated hovering. One study of pa-
tients taking warfarin deployed a
home-based pill dispenser that
was electronically tethered to a
lottery system. Patients were au-
tomatically entered into a daily
random drawing, with a small
chance of winning $100 and a
larger chance of winning $10.
Each day, patients were electroni-
cally notified if their number had
come up — which it would do
about 1 day in 5 — but were eli-
gible for the prize only if they
had taken their warfarin the pre-
vious day, as signaled by the dis-
penser. The system provided daily
engagement, the chance of a prize,
and a sense of anticipated regret:
no one wants to receive news of
winning only to be disqualified
for nonadherence the previous day.
The expected value of the lottery
was less than $3 per day, but the
system reduced the rate of incor-
rect doses from 22% to about 3%
and reduced the rate of out-of-
range international normalized
ratios from 35% to 12%.
4
Such a
system could easily be deployed
to improve medication adherence
among patients discharged from
the hospital with congestive heart
failure or after being treated for
acute coronary syndromes. This
system uses technology with an
engagement strategy informed by
behavioral economics to hover
over patients.
In another clinical trial, pa-
tients with difficult-to-control di-
abetes were randomly assigned to
receive usual care or mentorship
from another patient who had
previously managed to tame his
or her own diabetes. The mentor
merely had to call the patient once
a week. The result at 6 months was
glycated hemoglobin levels more
than a full percentage point lower
than those in the control group,
created by a system requiring
minimal technology to produce
hovering that was “automatic”
from the clinician’s perspective.
5
This kind of hovering must be
targeted to the right clinical and
social circumstances. The biggest
savings will probably come from
reducing preventable hospitaliza-
tions or delaying entrance into
nursing homes, because that’s
where so much spending current-
ly occurs. However, cell-phone
mentors and automatic pill-bot-
tle reminders probably won’t of-
fer much to patients who are fre-
quently hospitalized owing to a
combination of severe illness and
challenging life circumstances.
These patients, at one end of the
spectrum of intensity of health
care needs, require a more per-
sonnel-intensive approach that fo-
cuses as much on social circum-
stances as on complex medical
care. The best targets for auto-
mated hovering are conditions
whose management depends sub-
stantially on individual patients’
behavior. Good targets are medi-
cation adherence in patients with
heart failure or acute coronary
syndromes and efforts to manage
diet, exercise, or weight. The
amount of hovering required to
engage patients in healthy behav-
iors during those 5000 hours will
depend critically on the intensity
of their needs, but automated
systems might be a cost-effective
solution for many patients.
There are potential concerns.
Some people might worry that
Auto mated Hover ing in He alth C are
The New England Journal of Medicine
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Copyright © 2012 Massachusetts Medical Society. All rights reserved.
n engl j med 367;1 nejm.org july 5, 2012
PERSPECTIVE
3
too much hovering will erode pa-
tients’ sense of personal respon-
sibility or that hovering in one
area might distract providers or
patients from other important
health issues. Others may worry
that hovering is too intrusive or
paternalistic — though patients
could easily opt out, and it’s ar-
guably no more paternalistic than
traditional approaches to improv-
ing patient outcomes. It will be
important to ensure that new
hovering efforts are evaluated
carefully, with assessment of both
intended and potential unintend-
ed consequences.
And of course, there is a con-
siderable amount we don’t know
about these approaches: the kinds
of patients, conditions, or set-
tings for which they will be the
most useful; the organizations
(hospitals, employers, or insur-
ers) that should be the ones to
deploy them; and how to make
them heard over the din of every-
thing else that competes for atten-
tion while remaining unintrusive
enough that nudges don’t become
self-defeating nags. There are both
clinical and research opportunities
in pursuing an approach that is
just as rigorous as our approach
to other areas of medicine. Care-
ful iterative testing is essential
because these new forms of pa-
tient engagement, whatever shape
they take, will be central to im-
proving population health in our
future health care system.
Disclosure forms provided by the authors
are available with the full text of this article
at NEJM.org.
From the Center for Health Equity Research
and Promotion, Philadelphia Veterans Af-
fairs Medical Center (D.A.A., K.G.V.), the
University of Pennsylvania Health System
Center for Innovations in Health Care Fi-
nancing (D.A.A., R.W.M., K.G.V.), the Penn
CMU Roybal P30 Center in Behavioral Eco-
nomics and Health (D. A.A., K.G.V.), the
Leonard Davis Institute of Health Econom-
ics, Center for Health Incentives and Behav-
ioral Economics (D.A.A., R.W.M., K.G.V.),
and the Perelman School and Wharton
School (D.A.A., K.G.V.), University of Penn-
sylvania — all in Philadelphia.
This article was published on June 20, 2012,
at NEJM.org.
1. McGinnis JM, Williams-Russo P, Knick-
man JR. The case for more active policy at-
tention to health promotion. Health Aff
(Millwood) 2002;21:78-93.
2. Gawande A. The hot spotters. The New
Yorker. January 24, 2011.
3. Volpp KG, Asch DA, Galvin R, Loewen-
stein G. Redesigning employee health incen-
tives — lessons from behavioral economics.
N Engl J Med 2011;365:388-90.
4. Volpp KG, Loewenstein G, Troxel A, et al.
A test of financial incentives to improve war-
farin adherence. BMC Health Serv Res 2008;
8:272.
5. Long JA , Jahnle EC, Richardson DM,
Loewenstein G, Volpp KG. Peer mentoring
and financial incentives to improve glucose
control in African American veterans: a ran-
domized trial. Ann Intern Med 2012;156:416-
24.
DOI: 10.1056/NEJMp1203869
Copyright © 2012 Massachusetts Medical Society.
Auto mated Hover ing in He alth C are
T
hree decades of research fo-
cused predominantly on
costs and the use of services
among Medicare beneficiaries
has repeatedly found wide re-
gional variations in health care
experiences and health system
performance.
1
Much less atten-
tion has been paid to variations
in access to care and their asso-
ciated implications for quality of
care and health outcomes. Our re-
cent Commonwealth Fund report,
“Rising to the Challenge: Results
from a Scorecard on Local Health
System Performance,”
2
shows that
when we look beyond state aver-
ages, there are staggeringly wide
gaps in people’s ability to gain ac-
cess to care in different commu-
nities around the country. We
also find a strong and persistent
association between access and
health care quality, including the
receipt of preventive care.
Simply put, where a person
lives matters — it influences the
ability to obtain health care, as
well as the probable quality of
care that will be received —
though it should not matter in an
equitable health care system. This
and other Scorecard findings have
important implications that are
relevant to national policy reforms
and to newly available resources
for improving access and quality
of care.
The Scorecard tracks 43 health
system performance measures
grouped into four dimensions:
access, prevention and treatment,
potentially avoidable hospital use
and cost, and healthy lives. The
analysis examined the range of
variation across all 306 hospital
referral regions (HRRs) — region-
al health care markets defined
with the use of patient-flow data
for the Dartmouth Atlas of Health
Care — and drew largely from
publicly available data, generally
from 2008 to 2010. (See the Sup-
plementary Appendix, available
Geographic Variation in Access to Care — The Relationship
with Quality
David C. Radley, Ph.D., M.P.H., and Cathy Schoen, M.S.
The New England Journal of Medicine
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Copyright © 2012 Massachusetts Medical Society. All rights reserved.