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Health Informatics

Prof. Timothy Bickmore

Health Informatics

US Healthcare Industry $2T/year

Much of the industry (especially delivery) remains in the dark ages of computing.

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Pre-Computer Medical Information Processing

Paper-based Medical Records

19th century as a personalized “lab notebook” / memory aide

Electronic Medical Recordsaka Electronic Health Recordsaka Computerized Patient Record Sys

Data acquired and created during a patient’s course through the healthcare systemElectronically maintained informationLifetime healthIntegrated, managed, within reminders, alerts, and linkagesShared among all health providers

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Integrated EMR

Example EHR – VA VistAVA cares for 26.5M US vets – largest provider in USVeterans Affairs Information System Technology and Architecture.Developed in early 1980s adopted by VA, DOD, HIS and a Finnish consortium.M-based application.Separate application packages: scheduling, pharmacy, text integration, radiology, lab, etc.

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5

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But… as of 2005

24% penetration of EMRs in outpatient clinics.5% of hospitals used computerized physician order entry

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Major Areas of Health Informatics

Medicine (Physician & Nurse)Public healthInsurance & AdministrationResearch (e.g., health services)BioinformaticsConsumer Informatics

Behavioral Informatics

Top 10 Accomplishments in Medical Informatics ACMI ‘00

1. MEDLINE2. UMLS3. Integrated clinical decision support systems 4. MUMPS5. QMR, MYCIN, etc.6. EMR systems7. Visible Human, Digital Anatomist8. HL-7 9. General problem solving methods10. Human genome project’s data

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UMLS Semantic Network

The Future (2008)(Greenes & Lorenzi ’98, JAMIA 5(5))

Deep integration of information technologiesAll research & collaboration mediated through digital information

Enterprise is fully networkedAnnual checkup: provider and patient review health logs and record of use, discuss patient’s understanding of health information, and model alternative health mgt strategies for the coming year

Health Care DeliverySystemic cost-effectiveness constant focus Delivery of care in ambulatory setting or home (telemed)

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Consumer & Behavioral Informatics

Trends

As we do a better job of keeping people alive…

People live longerShift from acute to chronic care

As we do a better job controlling costs…Shift from inpatient to outpatient care

Projections indicate significant shortages in doctors and nurses in the future

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(One) Motivation for Consumer Informatics

“I Have a Chronic Condition”

15%

24%

35%

58%66%

0%

10%

20%

30%

40%

50%

60%

70%

18-29 30-39 40-49 50-64 65+Chronic Illness and Caregiving Survey, Harris, 2000

Percentage of Americans Saying…

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Medical Regimen Adherence

Medication adherence rates in monitored clinical trials range from 43%-78% (overall average ~50%).Rates drop for polypharmacy.33-69% of all medication-related hospitalizations are due to non-adherence, costing $100B/y30-80% of adults in US have functional health literacy problems

In 2000, Tobacco use leading cause of death, killing 435,000 people, or 18.1% of everyone who died (CDCP, 2004 Web site) Poor diet and physical inactivity caused 400,000 deaths, or 16.6% of the total (up 100,000 deaths since 1990)64% of the population, is overweight or obese, putting them at higher risk of heart disease, diabetes, some types of cancer Obesity & overweight cost the nation $117 billion in 2000 In 1998, the cost of obesity was about 78.5 billion which was 9% of the total annual US medical procedures (Finkelstein et al 2003)According to Rand Corporation that if Americans continue to get fatter at current rates, by 2020 about one in five health care dollars spent on people aged 50 to 69 could be due to obesity – 50% more than now.

“Lifestyle health behavior”

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Ramifications

People will need to take more responsibility for their own care.But, they can’t even take their medicine as prescribed, let alone stop smoking or avoid sun exposure, or eat well, or exercise…

Can technology help?

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Behavioral Informatics

“the study of the uses of information and communication technologies by patients and health care providers as well as the study of the design, implementation, and evaluation of behavior change interventions delivered through advanced technologies...”

SBM Behavioral Informatics SIG

Early ExampleEarly example: tailored printInformation acquired from userInformation in letter or pamphlet is tailored based on

Stage of change (for example)Progress since last assessment

Several interventions and studies completed since 1990

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Tailored print - resultsReview of the literature - 6 out of 9 computer based materials had positive effects (p<.10). (Strecher, 1999)Tailored print material resulted in higher cessation rates for light-mod smokers vs. non-tailored material (Strecher, 1994)Prochaska and colleagues (2001) compared tailored print vs. tailored print plus telephone counselor vs. counselor only.

tailored + counselor outperformed tailored alone at 0,6, 12 but not 18 month. At 18 months the tailored print had abstinence rates of 23.2%

Some Other Examples of Behavioral Informatics

Intelligent biomedical devicesOn-line support groupsAutomated therapyPedagogical dramaWeb-based content

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Focus

Automated Systems that interact directly with patients using natural language to achieve health behavior change

Allows application of widest range of behavior change theories to widest range of patientsLowest cost (no provider in the loop)Most analogous to “gold standard”

Example –Telephone Linked Care (TLC)

• Series of studies by Dr. Robert Friedman and colleagues at Boston University• Since 1990’s

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Telephone Linked Care

TLC

EMR

CMSServer

DB

CMSClient

Applied To• Exercise adoption• Smoking cessation• Diet (general, low fat)• Weight management• Antidepressant adherence• Mammography screening• Alcohol use screening• Hypertension med adherence• Angina Pectoris management• CHF management• COPD management• Asthma management• Diabetes management• Depression management• High risk pregnancy mgt• Chronic disability mgt• Alzheimer’s caregiver support

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Typical TLC Conversation

Asks Questions of the UserTLC Comments on User’s Responses to Its QuestionsProvides Information to UserCounsels UserGoals and/or Homework is setPrevious Goals / Homework is reviewed and adjusted

HUMAN TLC

* p < .04 vs. Control

Control

Baseline

6 mos.

Mod

+ A

ctiv

ity (

min

/wk)

0

30

60

90

120

150

180

210(186)

(170)

(101)

TLC-Physical Activity

(122)

(164)

(183)

12 mos.

King AC, Friedman RH, et al. Annals of Behavioral Medicine. Spring 2003

****

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Health Buddy

State of the Art:Scripted Interactions

Scripts written by teams of expertsRepresented as flow charts or Augmented transition networksImplemented as state machines / ATNs / VXML

GetCommitment

GC_6

I'm going to workout at the gym.

Great. How much aerobic exercise do you plan to do?

GC_4

X again?

No

How long do you plan to play for?

Yes

GC_END

Great. How long do you plan to go for?

GC_7

I'm going to go for a walk.

Great. How long do you plan to go for?

GC_8(null)

(below exp)

(at exp)

Yes

Do you think you can go for X minutes?Do you think you can increase your time a little today/tomorrow?Can you keep up the same time as yesterday/etc?

GC_12(null)

GC_10

if REL & know location

No

yes

Where are you going to walk?

Are you going to (location X) again?

Who?

(if REL & know buddy)

(else)

No

Yes

yes

No

(loner)

MotivateDuration

No

MotivateToExercise

GC_1(null)

I'm going to play a sport.

if REL & know sport

GC_2

GC_3

GC_5

GC_9

GC_11

GC_13

GC_14

GC_15

What kind of exercise are you going to do?

Are you going to workout tomorrow?

Yep

GC_START(null)

Are you going to get some [more] exercise today?

(time2bed<2)

(time2bed > 2)

no yes

GC_16

GC_17

GC_18

Something else.

(TEXTENTRY)What kind of exercise?

GC_19

Great.

Which one?

Are you going to gowith X again?

Are you going togowith anyone?

I can't

Is it becuaseof your illness/injury?

(no illness/injury)

(illness|injury)

Yes

No

GC_19

GC_20

GC_21

GC_22

(above exp)

No, I really want to.OK

You shouldn't try to do somuch so soon... How about

X minutes this time?

OK, but you should tryto increase gradually..

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(some) Research inBehavioral Informatics

Helping Relationships

Therapeutic/Working alliance has significant effects on

Client satisfactionClient adherenceOutcomes

Most importantfactor in buildingalliance is providerempathy

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Embodied Conversational Agents(ECAs)

Emulate human face-to-face conversationFocus on nonverbal communicative behavior

Hand gestureGazeEyebrow and head motionPosture shiftsIntonationFacial display

Relational Behavior

Health CommunicationIdentifying empathic opportunities

PsychotherapyUnconditional positive regardEmpathic listening

Social Psychology Social penetration theory / self-disclosureMeta-relational communicationContinuity behaviors

SociolinguisticsPoliteness theory

Linguistics / Conversation Analysis

Structure & function of social dialogue

CommunicationComforting behaviorNonverbal immediacy behavior

Change Over TimeIncreasing common groundIncreasing intimacyDecreasing politeness

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Geriatric FitTrack Study

H1 – Older adults will accept, use and enjoy the relational agent system.

H2 – Users will perform more physical activity compared to a control group.

BickmoreCHI ‘05

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Study Protocol

BMC – South Boston Safety Net HospitalTwo month daily contact interventionSubjects referred from geriatric clinicStand-alone computer & table providedControl: standard-of-care (brochure)Measures

Pedometer readings (steps)Likert scale questions about system & LauraComputer logsDemographics & SF-12Semi-structured interviews

Subjects

Randomized 10 to RELATIONAL 11 to CONTROL

2 men, 19 womenAge 63-85 (mean 74)76% African American77% Overweight or Obese86% Low Reading Literacy

38% Never Used a Computer (50% in REL)29% Used Computer a “Few Times”

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Results: Ease of Use

Easy Difficult1 2 3 4 5 6 7

“That is so easy. That is so good. Regular computers I don't do. But, that was so easy, even a baby could do that.”

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Results: Satisfaction

Satisfaction with Laura

Not at all Very1 2 3 4 5 6 7

5.4

Desire to Continue

Not at all Very1 2 3 4 5 6 7

6.4

Satisfaction with Overall System

Not at all Very1 2 3 4 5 6 7

6.3

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Results: Relationship with Laura

Liking of Laura

Not at all Very1 2 3 4 5 6 7

6.3

Relationship with Laura

Stranger Close Friend

1 2 3 4 5 6 7

5.6

6.4

Trust in Laura

Not at all Very1 2 3 4 5 6 7

Results: Impressions of Laura

Friendly

Not at all Very1 2 3 4 5 6 7

6.8

Repetitive

Not at all Very1 2 3 4 5 6 7

4.8

Understands Me

Not at all Very1 2 3 4 5 6 7

5.4

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Results: Walking

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 2 4 6 8 10

R

Week

Ste

ps W

alke

d

RELATIONAL

CONTROL

Difference In slopes p=.004

Geriatrics Study Results

H1: Most subjects liked the system & Laura“I enjoyed her, I enjoyed Laura, and I'm quite sure somebody else would.”

H2: Intervention subjects performed more physical activity.

“It was the best thing that happened to me, to have something that pushed me out and get me walking.”

Continuing work under NSF CAREER grant.

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Just-in-Time Informationfor Exercise Adoption

NLM R21

Why Wearable?

Available at time and place of need

With integrated sensors ⇒Able to initiate interactionAble to conduct context-tailored interaction

Better relationshipAvailability & Contact time

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Just In TimeField Study

Research questions:Is intervening at the moment of decision making more effective than at the end of the day?Is a relational agent more effective than text?

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PDA-based Health AdvisorDesign Studies

Modality StudyTask Interruption Studies

Modality Study

Compared 4 modalities:Text onlyText + Static agent imageAnimated agentAnimated agent + nonverbal sounds

Backchannels, Discourse markers, etc.

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Modality Study

Animated agent also scored higher (approaching significance) on credibility of health information and comfort using in the workplace.

11.5

22.5

33.5

44.5

5

TEXTIMAGE

ANIMFULL

WAI (p=.008)

11.5

22.5

33.5

44.5

5

TEXTIMAGE

ANIMFULL

WAI (p=.008)

11.5

22.5

33.5

44.5

5

TEXTIMAGE

ANIMFULL

CARING (p<.05)

11.5

22.5

33.5

44.5

5

TEXTIMAGE

ANIMFULL

CARING (p<.05)

Task Interruption and Health Regimen Adherence

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Cueing

aka Stimulus ControlBehavioral Technique Presenting a conditional stimulus to perform a desired health behaviorUsed by most people changing health behaviorOne of the most effective techniques

Task Interruption

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Research Question

What is the best way to interrupt someone at work get them to perform a healthy behavior?

Hypotheses

Politeness

Shor

t-ter

m C

ompl

ianc

e

Politeness

Long

-term

Usa

ge

Politeness

Long

-term

Adh

eren

ce

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Experimental Design

Series of Studies with Common Framework

Primary task: web browsing & typing on PCSecondary task: “wrist rests”

Experimenter: motivates performance of primary taskAgent: interrupts periodically and motivates performance on secondary task

Experimental DesignMeasures

Manipulation checkSelf-reported politeness/annoyingness of agent

Short-term complianceRest timeSelf-reported effectiveness of agent

Long-term adherenceSelf-reported desire to continue

Attitude towards agentLiking, satisfaction, comfort, etc.

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Studies

Each 4-treatment counterbalanced within-subjects designEach treatment:

Intro dialog with agentWork at primary task for 10 minutesAgent interrupts twice and asks them to take a wrist rest

Studies

Study 1: Vary politeness of interrupt audio tone

4 tones pre-tested on annoying-polite scale

AUDIO1 AUDIO2 AUDIO3 AUDIO4

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Studies

Study 2: Compare 4 methods from literature on task interruption

BaselineForewarningNegotiated (“snooze” button)Social

More chatty/empathetic introApologize for interrupt Ask how feeling & empathize

Sorry to interrupt, but it’s time for your next rest. …How stressed are you feeling right now? …Sorry to hear…

Participants

Study 1N=2952% female, 83% students, aged 18-30 StageOfChange: preparation (but wide variation)

Study 2N=16Demographics identical

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Results – Study 1Self-report Measures

1

2

3

4

5

6

7

AUDIO1 AUDIO2 AUDIO3 AUDIO4

Mea

n R

atin

g

P OLITEANNOYINGSATISFIEDCONTINUECOMFORTLIKE

N=29

Results – Study 1Behavioral

10

11

12

13

14

15

16

17

18

19

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AUDIO1 AUDIO2 AUDIO3 AUDIO4

Res

t Tim

e (s

econ

ds) T1 (n.s.)

T2 (p<.05)

Rest Time

3.2

3.25

3.3

3.35

3.4

3.45

3.5

3.55

3.6

3.65

3.7

3.75

AUDIO1 AUDIO2 AUDIO3 AUDIO4

Prim

ary

Task

s Com

plet

ed

(n.s.)

Primary Tasks

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Conclusions – Study 1

Politeness was perceivedPoliteness / Long-term Adherence relationship confirmedPoliteness / Short-term Compliance relationship more complex

Hypothesized relationship seems to hold on first exposure, but not significantBy second exposure, may be seeing longer-term effects of very annoying => non-use relationship

Results – Study 2Self-report Measures

1

2

3

4

5

6

7

BASELINE FOREWARN NEGOTIATED SOCIAL

Mea

n R

atin

g

ANNOYPOLITEEFFECTIVECONTINUE

(a.s.)

N=16

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Results – Study 2Behavioral

15

17

19

21

23

25

27

29

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BASELINE FOREWARN NEGOTIATED SOCIAL

Res

t Tim

e (s

econ

ds)

T2 (n.s.)

T1 (a.s.)

Rest Time

3.5

3.7

3.9

4.1

4.3

4.5

4.7

4.9

BASELINE FOREWARN NEGOTIATED SOCIALPr

imar

y Ta

sks C

ompl

eted

Primary Tasks

(p<.001)

Conclusions – Study 2SOCIAL was most polite; FOREWARN least.

FOREWARN had greatest compliance on first exposure, but lowest on second (similar to AUDIO4).

Desire to CONTINUE greatest for SOCIAL; lowest for BASELINE. SOCIAL resulted in greatest compliance (T2, but n.s.) NEGOTIATED had high levels of compliance AND low disruption to primary task.Conclusion: Combination of Social & Negotiated may be best

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Overall Conclusions

Politeness important for maintaining long-term adherence (desire to continue use).Social/empathic behavior important for maximizing compliance.

Next Steps

Manipulate social distanceBrown & Levinson Politeness Theory predicts less politeness will be acceptable

Combine with contextual informationLongitudinal trials

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In collaboration with Boston Medical Center

Funded by NHLBI

“Virtual Nurse”

Hospital DischargeHospital Discharge is a ‘Transition’ leading to:High rates of medical errorsBy 30-days:-- 20% Adverse Events (1/3 preventable)-- 17% Visit ED-- 19.7% Rehospitalized

Problem

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Are Patients Well Prepared For

Discharge?

Of post-hospital patients:

37.2% able to state the purpose of their medications

41.9% able to state their diagnosis

Mayo Clinic ProceedingsAugust 2005; 80(8):991-994

Principles of the Re-Engineered Hospital

Discharge

Ten discrete, mutually reinforcing, components:

Patient EducationReconcile Discharge Plans with National GuidelinesOutstanding Tests and StudiesMedication ReconciliationPost Discharge ServicePhysician AppointmentsWhat to do if a problem arisesAssess understandingDischarge Plan to Pt and Summary to PCPTelephone re-enforcement

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Adopted by the National Quality Forum as one of 30 "Safe Practices" (SP11)

Passed period of public comment (by the several thousand hospitals involved) and is now awaiting approval by the executive committee.

AHRQ-funded study now underway at BMC to evaluate manual implementation of this practice.

Current Status

Automated Discharge Nurse

NHLBI-funded study: Automate the Discharge Education Process to make the practice easier to disseminate

Focus on chronic cardiopulmonary diseasesEnsure reach to patients with low health literacy

Review with patient prior to hospital discharge:Medical conditionMedications & regimenetc

Confirm comprehensionRelay questions to human nurseFollow-up reminders with IVR system

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Planned Study (’07-’09)

Automated DA

Control

Human DA Total N=760

• Outcomes: AEs, ED visits, hospital re-admits • Substudies: relational behavior (“bedside manner”)

ethnic concordance

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Medication Adherence for Young Adults with Schizophrenia

Funded by Eli Lilly Pharmaceuticals - In collaboration with University of Pittsburgh School of Nursing

30 day interventionPilot study: 20 subject quasi-experimental trial

Trust & Social support especially importantIntervening on 3 behaviors in parallel

Medication adherence (one tracked med)System usePhysical Activity

Conclusion

• Behavioral informatics is an important interdisciplinary area of research.

• More infobickmore@ccs.neu.eduwww.ccs.neu.edu/home/bickmore/