Decisional architectures june 2013

Decisional Architectures
Bradford W. Hesse

June 19, 2013
Tuesday, October 29, 2013

Two Cases


No Insurance -- ER Presentation

Two Cases


•

Woman (Edna) presents with detached
breast. Explains that she has known
about problem for nine years. Cancer is
advanced; now fatal. Death would have
been avoidable with preemptive care.

No Insurance -- ER Presentation

Two Cases

•

Woman (Edna) presents with detached
breast. Explains that she has known
about problem for nine years. Cancer is
advanced; now fatal. Death would have
been avoidable with preemptive care.

Private Insurance -- Cadillac Care

•


Woman (Helen), age 50, detects a lump
in breast and is sent for a mammogram
with follow-up needle biopsy. Tumor at
4 centimeters, estrogen- and
progesterone-receptor negative
diagnosed at Stage II. Private oncologist
prescribes aggressive treatment with
prolonged hospital stays, complications,
nosocomial infections, depletion of
insurance and eventually death.

Symptom
Checklist

Preventable
medical
error:
✦ accounts
for
48,000
to
98,000
deaths
per
year

(1999
IOM
esAmate)
✦ results
in
$17.1
billion
in
extraneous
costs
per

year
(Health
Aﬀ.
Apr
2011;30(4):596-‐603).
✦ most
errors
occur
from
communicaAon

breakdowns
(Mazor
et
al,
2012)

4

Symptom
Checklist

Preventable
medical
error:
✦ accounts
for
48,000
to
98,000
deaths
per
year

(1999
IOM
esAmate)
✦ results
in
$17.1
billion
in
extraneous
costs
per

year
(Health
Aﬀ.
Apr
2011;30(4):596-‐603).
✦ most
errors
occur
from
communicaAon

breakdowns
(Mazor
et
al,
2012)
Public
health
awareness
system:
✦ Lack
of
adherence
to
public
health

recommendaAons
(smoking
cessaAon,
diet,

exercise,
adherence
to
screening

recommendaAons)
doubles
mortality
rates
✦ Lack
of
“medical
home”
for
preempAve
support

leads
to
paAent
confusion,
fragmented
care,

personal
de-‐acAvaAon,
and
debilitaAng

outcomes.

5

Symptom
Checklist

Preventable
medical
error:
✦ accounts
for
48,000
to
98,000
deaths
per
year

(1999
IOM
esAmate)
✦ results
in
$17.1
billion
in
extraneous
costs
per

year
(Health
Aﬀ.
Apr
2011;30(4):596-‐603).
✦ most
errors
occur
from
communicaAon

breakdowns
(Mazor
et
al,
2012)
Public
health
awareness
system:
✦ Lack
of
adherence
to
public
health

recommendaAons
(smoking
cessaAon,
diet,

exercise,
adherence
to
screening

recommendaAons)
doubles
mortality
rates
✦ Lack
of
“medical
home”
for
preempAve
support

leads
to
paAent
confusion,
fragmented
care,

personal
de-‐acAvaAon,
and
debilitaAng

outcomes
Living
with
cancer
✦ Failures
to
remain
vigilant
result
in
recurrence

✦ InaXenAon
to
sequelae
from
treatment
reduces

quality
of
life,
makes
vulnerable
to
co-‐morbidity
✦ Lack
of
coordinated
care
leaves
paAent
feeling

“lost
in
transiAon,”
abandoned,
alone,
and

prone
to
post-‐traumaAc
symptoms.


6

Institute of Medicine’s “Crossing the
Quality Chasm” Report


The Psychology of System Design

Knowledge in the Head*
Knowledge in The World*

*Norman, D. A. (1988). The psychology of everyday things. New York: BasicBooks.

What happens with bad design?


Attention to Decision Architectures
Has Become Essential in I.T. Enabled
Business

“UX” = (User Interface)


Successful Architecture in Business
Tracking for
transparency,
accountability
Relational cues
to promote
trust

Ease of use
makes desired
behavior easy

Browsing to
encourage
familiarity

Participatory
options
encourage
engagement
Multiple delivery
options for
personalized service

But what about
medicine?


Many Computer Systems in Medicine
Follow Anachronistic Assumptions
Wrong Question:
X What can the computer do?
X How do we automate cognition?
X What is the transactional gain?
X How do we get users to conform?
Better Questions:
✓ What can humans do?

✓
✓
✓

How do we augment cognition?
What is the relational gain?
How do we optimize
sociotechnical balance?

Source: Hesse BW, Shneiderman B. eHealth research from the user's perspective. Am J Prev Med 2007;32(5 Suppl):S97-103.

National Research Council Report
Advocates for “Rebalancing Investments”

January 2009

Computational Technology for Effective Health
Care advocates re-balancing the portfolio of
investments in health care IT

•
•
•


Greater cognitive support for physicians,
patients, and caregivers
Observing user-centered design principles
Accelerating research related to health
care in the computer and social sciences
and in health/biomedical informatics

“What we thought about EHRs was
wrong; beneﬁt came from restructuring
care.”

Edward H. Wagner, M.D.,
M.P.H., F.A.C.P.

Source:
InformaAcs
for
Consumer
Health
Summit,
November
5-‐6,
Potomac,
MD.

But progress in behavioral support still
lags -- leading to physician outcry.


Progress for patient support lags even
further, leading to vocal dissatisfaction.

The Case
of Hugo


Eric Topol, a cardiologist who directs the Scripps
Translational Science Institute in San Diego, says apps
that monitor blood pressure or glucose rates can be
more valuable than prescriptions to keep these
conditions in check.
"When we use a medication, we don't know if it's going
to work or not. It's much better when a person's taking
their blood pressure on a frequent basis," says Topol.
"The average person looks at their smartphone 150
times a day, so all of a sudden they're able to diagnose
if their blood pressure's adequately controlled and what
are the circumstances when it's not."


Creating a new “ecology” of decision
support technologies.

Source: Hesse BW, Hansen D, Finholt T, Munson S, Kellogg W, Thomas JC. Social Participation in Health 2.0. IEEE Computer. 2010;43(11):45-52.

How about
cancer care?


Architectural Failures in Cancer Care

Zapka JG, Taplin SH, Solberg LI, Manos MM. A framework for improving the quality of cancer care: the case of breast and cervical cancer screening. Cancer Epidemiol Biomarkers Prev. 2003;12:4–13.
Zapka JG, Puleo E, Taplin SH, Goins KV,Yood MU, Mouchawar J, et al. Processes of care in cervical and breast cancer screening and follow-up--the importance of communication. Prev Med. 2004;39:81–90.
[PubMed]

The ASCO Blueprint: November 2011

Oncology as information
science: “The Learning
Health Care System”


Subjective
• Chief complaint
• Patient Reported Outcomes

Objective
• Clinical measures
• Laboratory findings
• Sensor data

Health Information Exchange

Hospital Based
EHR Data

Hospital Based
EHR Data

Assessment
• Diagnosis
• Categorical reporting
• Prognosis
Plan
• Treatment planning
• Self-care planning
• Post treatment
• Surveillance

Decision
Support
Needs


Medical
Team

Hospital
System

• Risk modeling
• Diagnostic support
• Treatment selection
• Guideline adherence
• Error detection/correction

Medical
Researcher

Patient
&
Family

• Situational awareness
• Population health
• Continuity of care
• Identify side effects
• Inform discovery

iNcentives

Understand mappings
Default
Give feedback
Expect error
Structure decisions
Source: Thaler RH, Sunstein CR. Nudge : improving decisions about health, wealth, and happiness. Rev. and expanded ed. New York:
Penguin Books; 2009.

iNcentives

Self Determination*

• Autonomy
• Mastery
• Connectedness

* E.g., Hesse BW. Enhancing Consumer Involvement in Health Care. In: Parker JC, Thornson E, editors. Health Communication in the
New Media Landscape. New York, NY: Springer Publishing Company; 2008. p. 119-149.

Understand mappings
• Navigation
• Illness Representations

* E.g., Hesse BW, Hanna C, Massett HA, Hesse NK. Outside the box: will information technology be a viable intervention to improve the
quality of cancer care? J Natl Cancer Inst Monogr. 2010;2010(40):81-89.

Understand mappings
• Navigation
• Illness Representations

* E.g., Finney Rutten LJ, Blake KD, Hesse BW, Augustson EM, Evans S. Illness Representations of Lung Cancer, Lung Cancer Worry,
and Perceptions of Risk by Smoking Status. J Cancer Educ. Jun 19 2011.

Default
Default access to healthcare
system:

• preventive services
• reminder systems
• shared decision-making
• easy communication
• up-to-date knowledge

* E.g., Hesse BW. Time to reboot: resetting health care to support tobacco dependency treatment services. Am J Prev Med. Dec 2010;39
(6 Suppl 1):S85-87.

Give Feedback

* E.g., Hesse BW, Hansen D, Finholt T, Munson S, Kellogg W, Thomas JC. Social Participation in Health 2.0. IEEE Computer. 2010;43
(11):45-52.

Expect Error


Structure Decisions

* E.g., Hesse BW, Suls JM. Informatics-enabled behavioral medicine in oncology. Cancer J. Jul-Aug 2011;17(4):222-230.

Discussion
•

How do we create interdisciplinary bridges between
medicine, behavioral science, systems engineering, and medical
informatics?
-- e.g., “Envisioning a Digital Future” report from the
President’s Council of Advisors on Science and Technology

•

How do we move decision science from the lab into realworld, high demand clinical environments?

•

How do we optimize the affordances of decisional
architectures to support the process demands of healthcare
reform (Accountable Care Organizations, Patient-Centered
Medical Home, Affordable Care provisions)?
-- “Health Information Technology in the U.S.: Driving Toward
Delivery System Change,” Robert Wood Johnson, 2012


Decisional architectures june 2013

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