Amy Waterbury
Kaiser Permanente
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Featured researches published by Amy Waterbury.
BMC Health Services Research | 2012
William M. Vollmer; Maochao Xu; Adrianne C. Feldstein; David H. Smith; Amy Waterbury; Cynthia S. Rand
BackgroundPharmacy databases are commonly used to assess medication usage, and a number of measures have been developed to measure patients’ adherence to medication. An extensive literature now supports these measures, although few studies have systematically compared the properties of different adherence measures.MethodsAs part of an 18-month randomized clinical trial to assess the impact of automated telephone reminders on adherence to inhaled corticosteroids (ICS) among 6903 adult members of a managed care organization, we computed eight pharmacy-based measures of ICS adherence using outpatient pharmacy dispensing records obtained from the health plan’s electronic medical record. We used simple descriptive statistics to compare the relative performance characteristics of these measures.ResultsComparative analysis found a relative upward bias in adherence estimates for those measures that require at least one dispensing event to be calculated. Measurement strategies that require a second dispensing event evidence even greater upward bias. These biases are greatest with shorter observation times. Furthermore, requiring a dispensing to be calculated meant that these measures could not be defined for large numbers of individuals (17-32 % of participants in this study). Measurement strategies that do not require a dispensing event to be calculated appear least vulnerable to these biases and can be calculated for everyone. However they do require additional assumptions and data (e.g., pre-intervention dispensing data) to support their validity.ConclusionsMany adherence measures require one, or sometimes two, dispensings in order to be defined. Since such measures assume all dispensed medication is used as directed, they have a built in upward bias that is especially pronounced when they are calculated over relatively short timeframes (< 9 months). Less biased measurement strategies that do not require a dispensing event are available, but require additional data to support their validity.Trial registrationThe study was funded by grant R01HL83433 from the National Heart, Lung and Blood Institute (NHLBI) and is filed as study NCT00414817 in the clinicaltrials.gov database.
Value in Health | 2016
David H. Smith; Maureen O’Keeffe-Rosetti; Ashli Owen-Smith; Cynthia S. Rand; Jeffrey O. Tom; Suma Vupputuri; Reesa Laws; Amy Waterbury; Dana Hankerson-Dyson; Cyndee Yonehara; Andrew E. Williams; Jennifer L. Schneider; John F. Dickerson; William M. Vollmer
OBJECTIVE Preplanned economic analysis of a pragmatic trial using electronic-medical-record-linked interactive voice recognition (IVR) reminders for enhancing adherence to cardiovascular medications (i.e., statins, angiotensin-converting enzyme inhibitors [ACEIs], and angiotensin receptor blockers [ARBs]). METHODS Three groups, usual care (UC), IVR, and IVR plus educational materials (IVR+), with 21,752 suboptimally adherent patients underwent follow-up for 9.6 months on average. Costs to implement and deliver the intervention (from a payer perspective) were tracked during the trial. Medical care costs and outcomes were ascertained using electronic medical records. RESULTS Per-patient intervention costs ranged from
BMC Public Health | 2015
Nancy Glass; Amber Clough; James Case; Ginger C. Hanson; Jamie S. Barnes-Hoyt; Amy Waterbury; Jeanne L. Alhusen; Miriam K. Ehrensaft; Karen Trister Grace; Nancy Perrin
9 to
Health Informatics Journal | 2011
Jennifer L. Schneider; Amy Waterbury; Adrianne C. Feldstein; Jerena Donovan; William M. Vollmer; Joan P. Dubanoski; Shelley Clark; Cynthia S. Rand
17 for IVR and from
Clinical Medicine & Research | 2011
William M. Vollmer; Andrew Williams; Suma Vupputuri; Cynthia S. Rand; David J. Smith; Adrianne C. Feldstein; Diane Ditmer; Jeffrey O. Tom; Reesa Laws; Jennifer L. Schneider; Amy Waterbury; Ashli Owen-Smith; Cyndee Yonehara
36 to
Focus on Alternative and Complementary Therapies | 2014
Ashli Owen-Smith; Cynthia S. Rand; David H. Smith; Jeffrey O. Tom; Reesa Laws; Amy Waterbury; William M. Vollmer
47 for IVR+. For ACEI/ARB, the incremental cost-effectiveness ratio for each percent adherence increase was about 3 times higher with IVR+ than with IVR (
Clinical Medicine & Research | 2013
William M. Vollmer; Cynthia S. Rand; Jeffrey O. Tom; Ashli Owen-Smith; David H. Smith; Suma Vupputuri; Andrew E. Williams; Diane Ditmer; Reesa Laws; Jennifer L. Schneider; Amy Waterbury
6 and
The American Journal of Managed Care | 2011
William M. Vollmer; Adrianne C. Feldstein; David H. Smith; Joan P. Dubanoski; Amy Waterbury; Jennifer L. Schneider; Shelley Clark; Cynthia S. Rand
16 for IVR and IVR+, respectively). For statins, the incremental cost-effectiveness ratio for each percent adherence increase was about 7 times higher with IVR+ than with IVR (
The American Journal of Managed Care | 2014
William M. Vollmer; Ashil A. Owen-Smith; Jeffrey O. Tom; Reesa Laws; Diane Ditmer; David H. Smith; Amy Waterbury; Jennifer Schneider; Cyndee Yonehara; Andrew Williams; Suma Vupputuri; Cynthia S. Rand
6 and
The Permanente Journal | 2016
Ashli Owen-Smith; David H. Smith; Cynthia S. Rand; Jeffrey O. Tom; Reesa Laws; Amy Waterbury; Andrew E. Williams; William M. Vollmer
43 for IVR and IVR+, respectively). Considering potential cost offsets from reduced cardiovascular events, the probability of breakeven was the highest for UC, but the IVR-based interventions had a higher probability of breakeven for subgroups with a baseline low-density lipoprotein (LDL) level of more than 100 mg/dl and those with two or more calls. CONCLUSIONS We found that the use of an automated voice messaging system to promote adherence to ACEIs/ARBs and statins may be cost-effective, depending on a decision makers willingness to pay for unit increase in adherence. When considering changes in LDL level and downstream medical care offsets, UC is the optimal strategy for the general population. However, IVR-based interventions may be the optimal choice for those with elevated LDL values at baseline.