Medications do not work in patients who do not take them. This true statement highlights the importance of medication adherence. Providers are often frustrated by the lack of consistent medication adherence in the patients they care for. Today with the time constraints that providers face, it becomes difficult to discover the extent of non-adherence. There are certainly many challenges in medication adherence not only at the patient-provider level but also within a healthy system and finally in insurers and payment systems. In a cross-sectional survey of unintentional nonadherence in over 24,000 adults with chronic illness, including hypertension, diabetes and hyperlipidemia, 62% forgot to take medications and 37% had run out of their medications within a year. These sobering data necessitate immediate policy and systems solutions to support patients in adherence. Medication adherence for cardiovascular diseases (CVD) has the potential to change outcomes, such as blood pressure control and subsequent events. The American Heart Association (AHA)/American Stroke Association (ASA) has a goal of improving medication adherence in CVD and stroke prevention and treatment. This paper will explore medication adherence with all its inherent issues and suggest policy and structural changes that must happen in order to transform medication adherence levels in the U.S. and achieve the AHA/ASA's health impact goals.
BACKGROUND: Individuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated. OBJECTIVE: This study aims to understand patients' preferences for mobile health (mHealth) technology and how that technology can be integrated into patients' routines, especially with regard to medication use. METHODS: We conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication-taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations. RESULTS: We conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants' mean glycated hemoglobin A(1c) control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized. CONCLUSIONS: The participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.
AIMS: HFrEF GDMT implementation remains incomplete. Non-cardiovascular hospitalization may present opportunities for GDMT optimization. We assessed the efficacy and durability of a virtual, multidisciplinary "GDMT Team" on medical therapy prescription for HFrEF. METHODS AND RESULTS: Consecutive hospitalizations in patients with HFrEF≤40% were prospectively identified from February 3 to March 1, 2020 (usual care group) and March 2 to August 28, 2020 (intervention group). Patients with critical illness, de-novo HF, and SBP<90mmHg were excluded. In the intervention group, a pharmacist-physician GDMT Team provided optimization suggestions to treating teams based on an evidence-based algorithm. The primary outcome was a GDMT optimization score, the net of positive (+1 for new initiations or up-titrations) & negative therapeutic changes (-1 for discontinuations or down-titrations) at hospital discharge. Serious in-hospital safety events were assessed. Among 278 consecutive encounters with HFrEF, 118 met eligibility criteria; 29 (25%) received usual care and 89 (75%) received the GDMT Team intervention. Among usual care encounters, there were no changes in GDMT prescription during hospitalization. In the intervention group, β-blocker (72% to 88%; P=0.01), ARNI (6% to 17%; P=0.03), MRA (16% to 29%; P=0.05), and triple therapy (9% to 26%; P<0.01) prescriptions increased during hospitalization. After adjustment, the GDMT Team was associated with an increase in GDMT optimization score (+0.58; 95% CI: +0.09 to +1.07; P=0.02). There were no serious in-hospital adverse events. CONCLUSIONS: Non-cardiovascular hospitalizations are a potentially safe and effective setting for GDMT optimization. A virtual GDMT Team was associated with improved HF therapeutic optimization. This implementation strategy warrants testing in a prospective randomized controlled trial. This article is protected by copyright. All rights reserved.
The prescribing of high-risk medications to older adults remains extremely common and results in potentially avoidable health consequences. Efforts to reduce prescribing have had limited success, in part because they have been sub-optimally timed, poorly designed, or not provided actionable information. Electronic health record (EHR)-based tools are commonly used but have had limited application in facilitating deprescribing in older adults. The objective is to determine whether designing EHR tools using behavioral science principles reduces inappropriate prescribing and clinical outcomes in older adults.
IMPORTANCE: Current approaches to predicting health care costs generally rely on a single composite value of spending and focus on short time horizons. By contrast, examining patients' spending patterns using dynamic measures applied over longer periods may better identify patients with different spending and help target interventions to those with the greatest need. OBJECTIVE: To classify patients by their long-term, dynamic health care spending patterns using a data-driven approach and assess the ability to predict spending patterns, particularly using characteristics that are potentially modifiable through intervention. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used a retrospective cohort design from a random nationwide sample of Medicare fee-for-service administrative claims data to identify beneficiaries aged 65 years or older with continuous eligibility from 2011 to 2013. Statistical analysis was performed from August 2018 to December 2019. MAIN OUTCOMES AND MEASURES: Group-based trajectory modeling was applied to the claims data to classify the Medicare beneficiaries by their total health care spending patterns over a 2-year period. The ability to predict membership in each trajectory spending group was assessed using generalized boosted regression, a data mining approach to model building and prediction, with split-sample validation. Models were estimated using (1) prior-year predictors and (2) prior-year predictors potentially modifiable through intervention measured in the claims data. These models were evaluated using validated C-statistics. The relative influence of individual predictors in the models was evaluated. RESULTS: Among the 329 476 beneficiaries, the mean (SD) age was 76.0 (7.2) years and 190 346 (57.8%) were female. This final 5-group model included a minimal-user group (group 1, 37 572 individuals [11.4%]), a low-cost group (group 2, 48 575 individuals [14.7%]), a rising-cost group (group 3, 24 736 individuals [7.5%]), a moderate-cost group (group 4, 83 338 individuals [25.3%]), and a high-cost group (group 5, 135 255 individuals [41.2%]). Potentially modifiable characteristics strongly predicted these patterns (C-statistics range: 0.68-0.94). For groups with progressively increasing spending in particular, the most influential factors were number of medications (relative influence: 29.2), number of office visits (relative influence: 30.3), and mean medication adherence (relative influence: 33.6). CONCLUSIONS AND RELEVANCE: Using a data-driven approach, distinct spending patterns were identified with high accuracy. The potentially modifiable predictors of membership in the rising-cost group represent important levers for early interventions that may prevent later spending increases. This approach could be adapted by organizations to target quality improvement interventions, particularly because numerous health care organizations are increasingly using these routinely collected data.
BACKGROUND: Diabetes is a leading cause of Medicare spending; predicting which individuals are likely to be costly is essential for targeting interventions. Current approaches generally focus on composite measures, short time-horizons, or patients who are already high utilizers, whose costs may be harder to modify. Thus, we used data-driven methods to classify unique clusters in Medicare claims who were initially low utilizers by their diabetes spending patterns in subsequent years and used machine learning to predict these patterns. METHODS: We identified beneficiaries with type 2 diabetes whose spending was in the bottom 90% of diabetes care spending in a one-year baseline period in Medicare fee-for-service data. We used group-based trajectory modeling to classify unique clusters of patients by diabetes-related spending patterns over a two-year follow-up. Prediction models were estimated with generalized boosted regression, a machine learning method, using sets of all baseline predictors, diabetes predictors, and predictors that are potentially-modifiable through interventions. Each model was evaluated through C-statistics and 5-fold cross-validation. RESULTS: Among 33,789 beneficiaries (baseline median diabetes spending: $4153), we identified 5 distinct spending patterns that could largely be predicted; of these, 68.1% of patients had consistent spending, 25.3% had spending that rose quickly, and 6.6% of patients had spending that rose progressively. The ability to predict these groups was moderate (validated C-statistics: 0.63 to 0.87). The most influential factors for those with progressively rising spending were age, generosity of coverage, prior spending, and medication adherence. CONCLUSIONS: Patients with type 2 diabetes who were initially low spenders exhibit distinct subsequent long-term patterns of diabetes spending; membership in these patterns can be largely predicted with data-driven methods. These findings as well as applications of the overall approach could potentially inform the design and timing of diabetes or cost-containment interventions, such as medication adherence or interventions that enhance access to care, among patients with type 2 diabetes.
BACKGROUND: Less than half of patients with cardiometabolic disease consistently take prescribed medications. While health insurers and some delivery organizations use claims to measure adherence, most clinicians do not have access during routine interactions. Self-reported scales exist, but their practical utility is often limited by length or cost. By contrast, the accuracy of a new 3-item self-reported measure has been demonstrated in individuals with HIV. We evaluated its concordance with claims-based adherence measures in cardiometabolic disease. METHODS: We used data from a recently-completed pragmatic trial of patients with cardiometabolic conditions. After 12 months of follow-up, intervention subjects were mailed a survey with the 3-item measure that queries about medication use in the prior 30 days. Responses were linearly transformed and averaged. Adherence was also measured in claims in month 12 and months 1-12 of the trial using proportion of days covered (PDC) metrics. We compared validation metrics for non-adherence for self-report (average <0.80) compared with claims (PDC <0.80). RESULTS: Of 459 patients returning the survey (response rate: 43.5%), 50.1% were non-adherent in claims in month 12 while 20.9% were non-adherent based on the survey. Specificity of the 3-item metric for non-adherence was high (month 12: 0.83). Sensitivity was relatively poor (month 12: 0.25). Month 12 positive and negative predictive values were 0.59 and 0.52, respectively. CONCLUSIONS: A 3-item self-reported measure has high specificity but poor sensitivity for non-adherence versus claims in cardiometabolic disease. Despite this, the tool could help target those needing adherence support, particularly in the absence of claims data.
Hospitals commonly provide a short-term supply of free P2Y12 inhibitors at discharge after myocardial infarction, but it is unclear if these programs improve medication persistence and outcomes. The ARTEMIS (Affordability and Real-World Antiplatelet Treatment Effectiveness After Myocardial Infarction Study) trial randomized hospitals to usual care versus waived P2Y12 inhibitor copayment costs for 1-year post-myocardial infarction. Whether the impact of this intervention differed between hospitals with and without pre-existing medication assistance programs is unknown.
Methods and Results
In this post hoc analysis of the ARTEMIS trial, we examined the associations of pre-study free medication programs and the randomized copayment voucher intervention with P2Y12 inhibitor persistence (measured by pharmacy fills and patient report) and major adverse cardiovascular events using logistic regression models including a propensity score. Among 262 hospitals, 129 (49%) offered pre-study free medication assistance. One-year P2Y12 inhibitor persistence and major adverse cardiovascular events risks were similar between patients treated at hospitals with and without free medication programs (adjusted odds ratio 0.93, 95% CI, 0.82-1.05 and hazard ratio 0.92, 95% CI, 0.80-1.07, respectively). The randomized copayment voucher intervention improved persistence, assessed by pharmacy fills, in both hospitals with (53.6% versus 44.0%, adjusted odds ratio 1.45, 95% CI, 1.20-1.75) and without (59.0% versus 48.3%, adjusted odds ratio 1.46, 95% CI, 1.25-1.70) free medication programs (Pinteraction=0.71). Differences in patient-reported persistence were not significant after adjustment.
While hospitals commonly report the ability to provide free short-term P2Y12 inhibitors, we did not find association of this with medication persistence or major adverse cardiovascular events among patients with insurance coverage for prescription medication enrolled in the ARTEMIS trial. An intervention that provided copayment assistance vouchers for 1 year was successful in improving medication persistence in hospitals with and without pre-existing short-term medication programs. Registration URL: https://www.clinicaltrials.gov/. Unique identifier: NCT02406677.
Pharmacy fill data are increasingly accessible to clinicians and researchers to evaluate longitudinal medication persistence beyond patient self-report.To assess the agreement and accuracy of patient-reported and pharmacy fill–based medication persistence.This post hoc analysis of the cluster randomized clinical trial ARTEMIS (Affordability and Real-world Antiplatelet Treatment Effectiveness After Myocardial Infarction Study) enrolled patients at 287 US hospitals (131 randomized to intervention and 156 to usual care) from June 5, 2015, to September 30, 2016, with 1-year follow-up and blinded adjudication of major adverse cardiovascular events. In total, 8373 patients with myocardial infarction and measurement of P2Y12 inhibitor persistence by both patient self-report and pharmacy data were included. Serum P2Y12 inhibitor drug levels were measured for 944 randomly selected patients. Data were analyzed from May 2018 to November 2019.Patients treated at intervention-arm hospitals received study vouchers to offset copayments at each P2Y12 inhibitor fill for 1 year after myocardial infarction.Nonpersistence was defined as a gap of 30 days or more in P2Y12 inhibitor use (patient report) or supply (pharmacy fill) and as serum P2Y12 inhibitor levels below the lower limit of quantification (drug level). Among patients in the intervention arm, a “criterion standard” definition of nonpersistence was a gap of 30 days or more in P2Y12 inhibitor use by both voucher use and pharmacy fill. Major adverse cardiovascular events were defined as adjudicated death, recurrent myocardial infarction, or stroke.Of 8373 patients included in this analysis, the median age was 62 years (interquartile range, 54-70 years), 5664 were men (67.7%), and 990 (11.8%) self-reported as nonwhite race/ethnicity. One-year estimates of medication nonpersistence rates were higher using pharmacy fills (4042 patients [48.3%]) compared with patient self-report (1277 patients [15.3%]). Overall, 4185 patients (50.0%) were persistent by both pharmacy fill data and patient report, 1131 patients (13.5%) were nonpersistent by both, and 3057 patients (36.5%) were discordant. By application of the criterion standard definition, the 1-year nonpersistence rate was 1184 of 3703 patients (32.0%); 892 of 3318 patients (26.9%) in the intervention arm who self-reported persistence were found to be nonpersistent, and 303 of 1487 patients (20.4%) classified as nonpersistent by pharmacy fill data were actually persistent. Agreement between serum P2Y12 inhibitor drug levels and either patient-reported (κ = 0.11-0.23) or fill-based (κ = 0.00-0.19) persistence was poor. Patients who were nonpersistent by both pharmacy fill data and self-report had the highest 1-year major adverse cardiac event rate (18.3%; 95% CI, 16.0%-20.6%) compared with that for discordant patients (9.7%; 8.7%-10.8%) or concordantly persistent patients (8.2%; 95% CI, 7.4%-9.0%).Patient report overestimated medication persistence rates, and pharmacy fill data underestimated medication persistence rates. Patients who are nonpersistent by both methods have the worst clinical outcomes and should be prioritized for interventions that improve medication-taking behavior.ClinicalTrials.gov Identifier: NCT02406677
Self-report of health conditions and behaviors is one potential strategy to increase the pace of enrollment into pragmatic clinical trials. In this study, we assessed the accuracy of self-reported poorly controlled hypertension among adults in the community who were screened for participation in the MedISAFE-BP trial. Of individuals who self-reported poorly controlled hypertension using the online trial enrollment platform, 64% had a systolic blood pressure less than 140 mm Hg when measured at home. Although we identified several characteristics associated with accurate self-report including older age (odds ratio [OR] 1.02 per year, 95% CI 1.01-1.03), diabetes (OR 1.59, 95% CI 1.17-2.14), and low health activation (OR 1.56 95% CI 1.17-2.07), we were unable to identify patients for whom self-reported hypertension would be a reliable method for their inclusion in a pragmatic trial.
Numerous factors are associated with the ability of patients with type 2 diabetes to achieve optimal glycemic control. However, many of these factors are not modifiable by quality improvement interventions. In contrast, the structure of how diabetes care is delivered, such as whether patients visit an endocrinologist or how prescriptions are filled, is potentially modifiable, yet its associations with glycemic control have not been rigorously evaluated.To investigate the association of diabetes care delivery with glycemic control in patients with type 2 diabetes using insulin.This retrospective cohort study used baseline claims and laboratory insurer data within a large pragmatic trial to identify individuals with type 2 diabetes using insulin with data for at least 1 hemoglobin A1c (HbA1c) test result from before trial randomization (July 1, 2014, to October 5, 2016) and for key nonmodifiable patient factors as well as diabetes care delivery and behavioral factors measured before the HbA1c test. Analyses were conducted from February 4, 2017, to November 13, 2018.Multivariable modified Poisson regression was used to evaluate the independent associations of nonmodifiable patient factors and potentially modifiable diabetes care delivery and patient behavioral factors with achieving adequate diabetes control (ie, HbA1c level <8%). The extent of measured variation explained in glycemic control by these factors was also explored using pseudo R2 and C statistics.Of 1423 patients included, 565 (39.7%) were women, and the mean (SD) age was 56.4 (9.0) years. In total, 690 (48.5%) had HbA1c levels less than 8%. Age (relative risk [RR] per 1-unit increase, 1.01; 95% CI, 1.00-1.02), persistent use of basal insulin (RR, 1.20; 95% CI, 1.00-1.43), more frequent filling of glucose self-testing supplies (RR, 1.01; 95% CI, 1.01-1.02), visiting an endocrinologist (RR, 1.41; 95% CI, 1.19-1.67), and receipt of insulin prescriptions by mail order (RR, 1.23; 95% CI, 1.03-1.48) were all independently associated with adequate control. Measured potentially modifiable diabetes care factors explained more variation in adequate glycemic control than measured nonmodifiable patient factors (C statistic, 0.661 vs 0.598; pseudo R2 = 0.11 vs 0.04).These findings suggest that for patients with type 2 diabetes using insulin, the way in which care is delivered may be more strongly associated with achieving adequate control of HbA1c levels than patient factors that cannot be altered are. Given the potential for intervention, these care delivery factors could be the focus of efforts to improve diabetes outcomes.
ABSTRACT Adherence to medications remains poor despite numerous efforts to identify and intervene upon non-adherence. One potential explanation is the limited focus of many interventions on one barrier. Little is known about the prevalence and impact of having multiple barriers in contemporary practice. Our objective was to quantify adherence barriers for patients with poorly-controlled cardiometabolic condition, identify patient characteristics associated with having multiple barriers, and determine its impact on adherence. We used a linked electronic health records and insurer claims dataset from a large health system from a recent pragmatic trial. Barriers to medication-taking before the start of the intervention were elicited by clinical pharmacists using structured interviews. We used multivariable modified Poisson regression models to examine the association between patient factors and multiple barriers and multivariable linear regression to evaluate the relationship between multiple barriers and claims-based adherence. Of the 1,069 patients (mean: 61 years of age) in this study, 25.1% had multiple barriers to adherence; the most common co-occurring barriers were forgetfulness and health beliefs (31%, n=268). Patients with multiple barriers were more likely to be non-white (Relative Risk [RR]:1.57, 95%CI: 1.21-1.74), be single/unpartnered (RR:1.36, 95%CI: 1.06-1.74), use tobacco (RR:1.54, 95%CI: 1.13-2.11), and have poor glycemic control (RR:1.77, 95%CI: 1.31-2.39) versus those with 0 or 1 barrier. Each additional barrier worsened average adherence by 3.1% (95%CI: -4.6%, -1.5%). In conclusion, >25% of non-adherent patients present with multiple barriers to optimal use, leading to meaningful differences in adherence. These findings should inform quality improvement interventions aimed at non-adherence.
The Affordability and Real-World Antiplatelet Treatment Effectiveness After Myocardial Infarction Study (ARTEMIS) cluster-randomized trial found that copayment reduction for P2Y12 inhibitors improved 1-year patient persistence in taking that medication.To assess whether providing copayment reduction for P2Y12 inhibitors increases patient persistence in taking other secondary prevention cardiovascular medications.This post hoc analysis of the ARTEMIS trial includes data from 287 hospitals that enrolled patients between June 2015 and September 2016. Patients hospitalized with acute myocardial infarction were included. Data analysis occurred from May 2018 through August 2019.Hospitals randomized to the intervention provided patients vouchers that waived copayments for P2Y12 inhibitors fills for 1 year. Hospitals randomized to usual care did not provide study vouchers.Persistence in taking β-blocker, statin, and angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker medications at 1 year, defined as the absence of a gap in medication supply of 30 or more days by pharmacy fill data in the intervention-arm (intent-to-treat) population.A total of 131 hospitals (with 5109 patients) were randomized to the intervention, and 156 hospitals (with 3264 patients) randomized to the control group. Patients discharged from intervention hospitals had higher persistence in taking statins (2247 [46.1%] vs 1300 [41.9%]; adjusted odds ratio, 1.11 [95% CI, 1.00-1.24]), and β-blockers (2235 [47.6%] vs 1277 [42.5%]; odds ratio, 1.23 [95% CI, 1.10-1.38]), although the association was smaller than that seen for P2Y12 inhibitors (odds ratio, 1.47 [95% CI, 1.29-1.66]). Persistence in taking angiotensin-converting enzyme inhibitors or angiotensin II-receptor blockers were also numerically higher among patients in the intervention arm than in the usual-care arm, but this was not significant after risk adjustment (1520 [43.9%] vs 847 [40.5%]; adjusted odds ratio, 1.10 [95% CI, 0.97-1.24]). Patients in the intervention arm reported greater financial burden associated with medication cost than the patients in the usual-care arm at baseline, but these differences were no longer significant at 1 year.Reducing patient copayments for 1 medication class increased persistence not only to that therapy class but may also have modestly increased persistence to other post–myocardial infarction secondary prevention medications. These findings have important implications for the clinical utility and cost-effectiveness of medication cost-assistance programs.ClinicalTrials.gov Identifier: NCT02406677
In the United States, there is well-documented regional variation in prescription drug spending. However, the specific role of physician adoption of brand name drugs on the variation in patient-level prescription drug spending is still being investigated across a multitude of drug classes. Our study aims to add to the literature by determining the association between physician adoption of a first-in-class anti-diabetic (AD) drug, sitagliptin, and AD drug spending in the Medicare and Medicaid populations in Pennsylvania.
We obtained physician-level data from QuintilesIMS Xponent™ database for Pennsylvania and constructed county-level measures of time to adoption and share of physicians adopting sitagliptin in its first year post-introduction. We additionally measured total AD drug spending for all Medicare fee-for-service and Part D enrollees (N = 125,264) and all Medicaid (N = 50,836) enrollees with type II diabetes in Pennsylvania for 2011. Finite mixture model regression, adjusting for patient socio-demographic/clinical characteristics, was used to examine the association between physician adoption of sitagliptin and AD drug spending.
Physician adoption of sitagliptin varied from 44 to 99% across the state’s 67 counties. Average per capita AD spending was $1340 (SD $1764) in Medicare and $1291 (SD $1881) in Medicaid. A 10% increase in the share of physicians adopting sitagliptin in a county was associated with a 3.5% (95% CI: 2.0–4.9) and 5.3% (95% CI: 0.3–10.3) increase in drug spending for the Medicare and Medicaid populations, respectively.
In a medication market with many choices, county-level adoption of sitagliptin was positively associated with AD spending in Medicare and Medicaid, two programs with different approaches to formulary management.
BACKGROUND: Pharmacies have a unique opportunity to address suboptimal adult vaccination rates, but few solutions have proven effective. Such strategies are challenged by the lack of access that many pharmacies have to a patient’s complete immunization history; consequently, they are unable to identify which of their patients actually require vaccination. A pharmacy-based strategy that leverages such information could enhance efforts to increase rates of guideline-based vaccination. OBJECTIVE: To determine the effect on vaccination rates of an automated telephonic intervention for adults in need of either pneumococcal vaccination or herpes zoster vaccination, or both. METHODS: Over a 1-year period, patients with identified vaccine gaps at 246 pharmacies of 3 pharmacy chains were randomly assigned to receive either usual care or an automated telephonic prompt for pneumococcal and/or herpes zoster vaccines based on patient records contained in state immunization registries and pharmacy data. The primary outcome was the proportion with administration of at least one of the vaccines offered between March 2016 and January 2017 based on intention-to-treat principles. Subgroup analyses included vaccination rates by age and sex. An as-treated analysis was also performed. RESULTS: 21,971 patients were included in the study, 57% of whom were female, with a mean age of 63 years. Vaccine administration proportions were 0.0214 (236/11,009) in the intervention group, and 0.0205 (225/10,962) in the control group (OR = 1.05, 95% CI = 0.87-1.26). Results did not differ in subgroup analyses based on patient age, sex, or individual pharmacy chain. Among intervention patients, 3,666 (0.333) completed the call by listening to the entire prompt. In an as-treated analysis comparing individuals who completed calls versus control, the intervention increased the odds of vaccination by 26% (OR = 1.26, 95% CI = 1.00-1.61). CONCLUSIONS: The automated prompt did not significantly increase vaccination rates. Potential barriers included intervention technical flaws, low rates of connecting with patients, insufficient follow-up by the pharmacy, and patients placing a relatively low priority on being vaccinated. DISCLOSURES: This project was funded by Pfizer and Merck through a grant from the Pharmacy Quality Alliance. Stolpe was an employee of the Pharmacy Quality Alliance at the onset of this project and an employee of Scientific Technologies Corporation during the data collection phase of the project. Stolpe has also served on the advisory board for Merck. Choudhry has no conflicts of interest to declare.
Purpose: We sought to determine whether an association study using information contained in clinical notes could identify known and potentially novel risk factors for nonadherence to antihypertensive medications.
Methods: We conducted a retrospective concept-wide association study (CWAS) using clinical notes to identify potential risk factors for medication nonadherence, adjusting for age, sex, race, baseline blood pressure, estimated glomerular filtration rate, and a combined comorbidity score. Participants included Medicare beneficiaries 65 years and older receiving care at the Harvard Vanguard Medical Associates network from 2010-2012 and enrolled in a Medicare Advantage program. Concepts were extracted from clinical notes in the year prior to the index prescription date for each patient. We tested associations with the outcome for 5013 concepts extracted from clinical notes in a derivation cohort (4382 patients) and accounted for multiple hypothesis testing by using a false discovery rate threshold of less than 5% (q < .05). We then confirmed the associations in a validation cohort (3836 patients). Medication nonadherence was defined using a proportion of days covered (PDC) threshold less than 0.8 using pharmacy claims data.
Results: We found 415 concepts associated with nonadherence, which we organized into 11 clusters using a hierarchical clustering approach. Volume depletion and overload, assessment of needs at the point of discharge, mood disorders, neurological disorders, complex coordination of care, and documentation of noncompliance were some of the factors associated with nonadherence.
Conclusions: This approach was successful in identifying previously described and potentially new risk factors for antihypertensive nonadherence using the clinical narrative.
Bronchoscopy is the safest procedure for lung cancer diagnosis when an invasive evaluation is required after imaging procedures. However its sensitivity is relatively low, especially for small and peripheral lesions. We assessed benefits and costs of introducing a bronchial gene-expression classifier (BGC) to improve the performance of bronchoscopy and the overall diagnostic process for early detection of lung cancer. We used discrete-event simulation to compare clinical and economic outcomes of two different strategies with the standard practice in former and current smokers with indeterminate nodules: (i) location-based strategy - integrated the BGC to the bronchoscopy indication; (ii) simplified strategy - extended use of bronchoscopy plus BGC also on small and peripheral lesions. Outcomes modeled were rate of invasive procedures, quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios. Compared with the standard practice, the location-based strategy (i) reduced absolute rate of invasive procedures by 3.3% without increasing costs at the current BGC market price. It resulted in savings when the BGC price was less than $3,000. The simplified strategy (ii) reduced absolute rate of invasive procedures by 10% and improved quality-adjusted life expectancy, producing an incremental cost-effectiveness ratio of $10,109 per QALY. In patients with indeterminate nodules, both BGC strategies reduced unnecessary invasive procedures at high risk of adverse events. Moreover, compared with the standard practice, the simplified use of BGC for central and peripheral lesions resulted in larger QALYs gains at acceptable cost. The location-based is cost-saving if the price of classifier declines. This article is protected by copyright. All rights reserved.
Background Many factors contribute to suboptimal diabetes control including insufficiently-intensive treatment and non-adherence to medication and lifestyle. Determining which of these is most relevant for individual patients is challenging. Patient engagement techniques may help identify contributors to suboptimal adherence and address barriers (using motivational interviewing) and help facilitate choices among treatment augmentation options (using shared decision-making). These methods have not been used in combination to improve diabetes outcomes. Objective To evaluate the impact of a telephone-based patient-centered intervention on glycosylated hemoglobin (HbA1c) control for individuals with poorly-controlled diabetes. Design Two-arm pragmatic randomized control trial within an explanatory sequential mixed-methods design. Subjects 1,400 participants 18–64 years old with poorly-controlled type 2 diabetes. Intervention The intervention was delivered over the telephone by a clinical pharmacist and consisted of a 2-step process that integrated brief negotiated interviewing and shared decision-making to identify patient goals and options for enhancing diabetes management. Main measures The primary outcome was change in HbA1c. Secondary outcomes were medication adherence measures. Outcomes were evaluated using intention-to-treat principles; multiple imputation was used for missing values in the 12-month follow-up. We used information from pharmacist notes to elicit factors to potentially explain the intervention’s effectiveness. Key results Participants had a mean age of 54.7 years (SD:8.3) and baseline HbA1c of 9.4 (SD:1.6). Change in HbA1c from baseline was -0.79 (SD:2.01) in the control arm and -0.75 (SD:1.76) in the intervention arm (difference:+0.04, 95%CI: -0.22, 0.30). There were no significant differences in adherence. In as-treated analyses, the intervention significantly improved diabetes control (-0.48, 95%CI: -0.91, -0.05). Qualitative findings provided several potential explanations for the findings, including insufficiently addressing patient barriers. Conclusions A novel telephone-based patient-centered intervention did not improve HbA1c among individuals with poorly-controlled diabetes, though as-treated analyses suggest that the intervention was effective for those who received it. Trial registration ClinicalTrials.gov NCT02910089