Does Shared Treatment Decision-Making Improve Asthma Adherence and

Outcomes?

Supported by grants from the National Heart, Lung and Blood Institute1R01 HL69358 (PI: SWilson) and 1R18 HL67092 (PI: ASBuist)

Only ~50% of patients take asthma medications at effective doses

Documented problems: Under-use of controller medications Over-use of relievers & OTC medications Poor inhaled medication technique Failure to fill/refill prescriptions Failure to keep medications available

when and where they are needed

Known contributors to non-adherence Patient

Younger age Low socioeconomic status Lack of education Memory problems Lack of understanding of the disease

Regimen Longer duration of treatment Higher cost Complexity, more frequent dosing Properties (bad taste, more side effects, etc.)

Physician-patient relationship Inadequate monitoring Failure to explain side effects Failure to analyze patient’s medication-taking

behaviors Failure to address the patient’s individual situation

and preferences

Models of Clinician-Patient Interaction Traditional model:

Interaction is directive; Clinician makes the treatment decision Evidence-based management usually follows a

traditional model

Informed decision-making model: Clinician provides information to the patient Patient makes the decision

Pt

MD

Pt

MD

Shared decision-making model: Mutual exchange of information and treatment

preferences between clinician & patient Both participate in treatment decisions Each brings unique knowledge to the interaction

Hypothesis: Involving patients in treatment decisions should result in:

Better adherence to treatment Better asthma control Greater patient satisfaction

PtMD

Design of the BOAT trial Three-arm, randomized controlled trial

SDM = shared decision making care management MBG = guidelines-based traditional care management UC = usual medical care

Data collection Baseline and 12-mos. post-randomization

QuestionnairePFT

12-mos. pre and 24 mos. post-randomization (36 mo.)Asthma medications dispensedAll health care utilization

BOAT study hypotheses regardingadherence and disease outcomes

SDM > MBG SDM > UC

Study OutcomesPrimary

Adherence to asthma medications

Asthma-related quality of life

Asthma-related health care utilization

Secondary Asthma control Use of reliever medications Symptom-free days; Lung function Satisfaction with asthma care Preferences, values, &

attitudes towards adherence Total asthma health care

utilization Asthma-related health care

costs

Both the SDM & MBG Interventions: Target patients with poorly controlled,

moderate-severe asthma Involve 2 in-person sessions, approximately 1

mo. apart, plus 3 follow-up calls at 3 mo. intervals Conducted by asthma care managers:

Clinical pharmacists Nurse practitioners and registered nurses Physician assistants Respiratory therapists

Parallel written protocols (scripts) guide both SDM and MBG clinician-patient interactions Structured to enable tailoring to the individual patient Instructional aides and worksheets are included in the

interventionist manual

SDM and MBG Interventions*

Provide information

Assess understanding of asthma Review asthma and how it is treated Confirm comprehension

Negotiate (SDM)/Prescribe (MBG)

• Summarize patient goals and priorities Review PFTs with patient Assess symptom control using objective criteria Determine asthma severity per GINA guidelines Define medication preferences Discuss +/- of each treatment option per patient

goals and preferences Negotiate a treatment decision

Wrap Up

Write Rx Give Asthma Action & Management Plan Teach proper inhaler use Give asthma diary Schedule follow-up appointment

Set the Stage

Establish rapport Describe session schedule Describe shared decision making approach

Gather patient information

Asthma symptoms Perceptions of control Medication use Use of alternative therapies Environmental triggers Patient goals & preferences

* White = MBG and SDM Gold = SDM only

Inclusion Criteria

Recent ED/hospital visit for asthma and/or evidence of over-use of rescue medication

18-70 years of age KFHP member ≥ 1 year Self-reported, doctor-diagnosed asthma Currently Rxed asthma medications Meets obstruction reversibility criterion One or more asthma control problems

(ATAQ score ≥1)

Exclusion Criteria

Mild intermittent/seasonal asthma Regular use of oral corticosteroids Currently receiving asthma care-management Not able to speak, read, and understand

English Planning to move out of area within two years

Eligible Patients

(N=613)

SDM (N= 204)

MBG (N= 205)

UC (N= 204)

Randomization*

* Adaptive randomization algorithm (Pocock, 1983) - ensures better than chance balanceand increases likelihood of better than chance balance on correlated characteristics.

Demographic characteristics*

N=613 %Age 18-34 yrs. 20

35-50 yrs. 4251-70 yrs. 38

Gender Male 44Female 56

Ethnicity Hispanic 4Asian 10Native Hawaiian/Pacific Islander

8

Black/African American

16

White/Caucasian 62

%Level of education

< High School Diploma 2HS Diploma/GED 16Technical/Some College 434-Year Degree/BA/BS 22Graduate Degree 17

Annual family income

$20,000 8$20,001 - $40,000 21$40,001 - $60,000 25$60,001 - $80,000 18$80,001 24DK/Refused To Answer 4

* No significant group differences.

38%

80%

Baseline asthma status*

* No significant group differences in symptom frequency, nocturnal symptoms, or FEV1 % predicted at baseline.

0

10

20

30

40

50

60

< 1/week

> 1/week but < daily

Daily

£ 2x/month

> 2x/month but < weekly

Weekly or m

ore often

> 80% of predicte

d

60-80% of predicte

d

< 60% of predicte

d

Perc

ent

SDMMBGUC

Symptom Frequency Nocturnal Symptoms FEV1 % predicted

De facto medication regimen and asthma control*

* No significant group differences at baseline.

Medication regimen Asthma Control

0

10

20

30

40

50

60

Mild intermittent

Mild persistent

Moderate persistent

Severe persistent

Well co

ntrolled

Moderately well co

ntrolled

Poorly controlled

Very poorly

controlled

Per

cent

SDM

MBG

UC

• Did the SDM patients’ medication choices differ from the MBG care managers’ guidelines-based Rx?

MedicationSDM

N=191MBG

N=186p-

value1

Beclomethasone 80 90 (50%) 108 (61%)Fluticasone 220 78 (43%) 53 (30%) 0.03Other ICS2 13 (7%) 17 (10%)

Any ICS 181 (95%) 178 (96%) 0.67

Leukotriene modifier 14 ( 7%) 14 (8%) 0.94Theophylline 4 ( 2%) 1 (1%) 0.37 Any Controller3 186 (97%) 181 (97%) 1.00

1. Chi-square or Fishers exact test. 2. Includes Beclomethasone and Fluticasone at lower strengths, and Budesonide.3. Includes ICSs, leukotriene modifiers, and theophylline; excludes LABAs and oral prednisone.

Adherence measure = Continuous Measure of Medication Acquisition (CMA)

CMA = Number of days’ supply of a medication dispensed/365 days

Proportion of days on which medication was available for use on Rxed regimen

A commonly used indicator of adherence to the intended daily regimen

Data from the HMO’s pharmacy database ~95% of patients obtain all their medications

from the HMO pharmacy

Cumulative medication acquisition (CMA) values pre and post randomization, by experimental

group

UC MBG SDM N p-valueBaseline Yr. N=203 N=203 N=204 N=610

Any ICS 0.32(0.32)

0.32(0.31)

0.33(0.34)

0.8986

Any ControllerN=2040.41

(0.47)

N=2050.38

(0.37)

N=2040.40

(0.43)

N=6130.9490

Follow-up Yr. N=203 N=202 N=204 N=609

Any ICS 0.39(0.37)

0.54(0.36)

0.62(0.38)

SDM vs MBG p=0.0162 SDM vs UC p<0.0001 MBG vs UC p<0.0001

Any Controller N=2040.49

(0.52)

N=2050.59

(0.45)

N=2040.69

(0.45)

N=613 SDM vs MBG p=0.0095 SDM vs UC p<0.0001 MBG vs UC p=0.0014

CMA index – Mean (SD)

Conclusions: For non-adherent patients with poorly

controlled asthma --

Involving patients in a meaningful way in treatment decisions does not result treatment regimens that conflict with standard guidelines, assuming patients have a basic understanding of: asthma their current level of disease control the medical rationale for asthma

treatment.

Conclusions: For non-adherent patients with poorly controlled

asthma, care management that utilizes a shared clinician-patient approach to selection of the treatment regimen significantly improves adherence to asthma controllers over a one year period when compared with both: usual medical care, and traditional, prescriptive care management

Intervention effects did not differ as a function of ethnic group (Caucasian, Asian and African American)

Conclusions - continued

Clinical approaches of asthma care managers can be shaped such that treatment decision making is shared with the patient in a meaningful way. This required use of a detailed intervention protocol,

training, and ongoing feedback.

Patients evaluate their own vs. the clinician’s influence on treatment decisions differently when they experience a shared decision making approach than when they experience prescriptive care management

Does shared decision-making lead to: better asthma control? better asthma-related quality of life? reduced asthma health care utilization? increased patient satisfaction?

Are adherence outcomes mediated by patient perceptions of their influence on treatment decisions?

Are disease outcomes mediated by medication adherence?

Questions being investigated by analyses in process

Decision Roles - Treatment decisions were made by:1 = Care manager alone2 = Care manager mostly3 = Patient and care manager equally4 = Patient mostly5 = Patient alone

Protocol Adherence -1 = Relevant elements not covered 3 = All elements covered, but some briefly,

incompletely, or inadequately5 = All topics covered completely,

thoroughly, and accurately

Process outcomes

Rating scales:

• How closely did interventionists follow the protocol• Who made the treatment decisions?

0

1

2

3

4

5

QC rater

QC rater

Care manager

Patients

SDM MBG

ProtocolAdherence

Decision Roles

ProtocolAdherence

Decision Roles

p=0.47

* p<0.001 **

*

Mea

n ra

ting

Investigators

Sandra Wilson, PhD, PI (PAMFRI, SUSM)Sonia Buist, MD, PI (OHSU, CHR)William Vollmer, PhD (CHR)Tom Vogt, MD (CHR)Nancy L. Brown, PhD (PAMFRI, SU)Philip Lavori, PhD (SUSM)Margaret Strub, MD (TPMG)Stephen VanDenEeden, PhD (KRFI/DOR)

ConsultantsAmiram Gafni, PhD Elizabeth Juniper, PhDCynthia Rand, PhDSean Sullivan, PhDKevin Weiss, MD

Clinical Site Co-investigators

Faith Bocobo, MD (TPMG)Christine Fukui, MD (TPMG)Donald German, MD (TPMG)John Hoehne, MD (TPMG) Matthew Lau, MD (TPMG)Myngoc Nguyen, MD (TPMG)

(SDM only)

Post-randomization CMA indices for inhaled corticosteroids, by group1

1. N=504. Excludes 4 patients with mild persistent asthma for whom no ICS was prescribed.2. Overall test of group differences, Wilcoxon/Kruskal Wallis test.3. Multiple comparisons: SDM vs. MBG, p=0.02; SDM vs. UC, p<0.0001; MBG vs. UC, p<0.0001.

MBG SDM UCGROUP

0.0

0.5

1.0

1.5

CM

A F

OR

ICS

Mn = 0.54 N = 202

Mn = 0.62 N = 204

Mn = 0.39 N = 203

Overall p<0.00012,3

Post-randomization CMA indices for all asthma controllers combined, by group1

1. N = 504. Excludes 4 patients with mild persistent asthma, for whom no controller was prescribed. 2. Overall test of group differences, Wilcoxon/Kruskal Wallis test.3. Multiple comparisons: SDM vs. MBG, p=0.02; SDM vs. UC, p<0.0001; MBG vs. UC, p=0.0023.

MBG SDM UCGROUP

0

1

2

3

CM

A F

OR

CO

NTR

OLL

ER

S Overall p<0.00012,3

Mn = 0.59 N = 205

Mn = 0.69 N = 204

Mn = 0.49 N = 204

Pre-randomization CMA for all controllers, by ethnicity, within relevant sites

African American White

0

1

2

3

CM

A 2

for C

ontro

llers

Asian White

ETHNICITY

0

1

2

3

CM

A 2

for C

ontro

llers

Northern CA & Portland Northern CA & Hawaii

Mn = 0.40 N = 94

Mn = 0.41 N = 344

Mn = 0.47 N = 205Mn = 0.36

N = 59

Post-randomization CMA for all controllers, by group, separately for Whites and Asians.

MBG SDM UCGroup

0

1

2

3

CM

A 2

for C

ontro

llers

MBG SDM UCGroup

0

1

2

3

CM

A 2

for C

ontro

llers

White Asian

Regression modelGroup comparison: p-value <=0.0001. Group x Ethnicity interaction: p-value = 0.4478

Mn=0.66 N = 68

Mn=0.74 N = 68

Mn=0.52 N = 69

Mn=0.78 N = 18

Mn=0.87 N = 19

Mn=0.52 N = 22

Post-randomization CMA for all controllers, by group, separately for Whites and African Americans

Regression modelGroup comparison: p-value <=0.0001; Group X Ethnicity interaction: p-value = 0.6993.

MBG SDM UC

Group

0

1

2

3

CM

A 2

for C

ontro

llers

MBG SDM UC

Group

0

1

2

3

CM

A 2

for c

ontro

llers

White African American

Mn = 0.63 N = 113

Mn = 0.74 N = 115

Mn = 0.53 N = 116

Mn = 0.55 N = 33

Mn = 0.51 N = 32

Mn = 0.34 N = 29