does shared treatment decision-making improve asthma adherence and outcomes?
DESCRIPTION
Does Shared Treatment Decision-Making Improve Asthma Adherence and Outcomes?. Supported by grants from the National Heart, Lung and Blood Institute 1R01 HL69358 (PI: SWilson) and 1R18 HL67092 (PI: ASBuist). Only ~50% of patients take asthma medications at effective doses. Documented problems: - PowerPoint PPT PresentationTRANSCRIPT
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