The relative benefits of claims and electronic health record data for predicting medication adherence trajectory

Franklin, Jessica M.; Gopalakrishnan, Chandrasekar; Krumme, Alexis A.; Singh, Karandeep; Rogers, James R.; Kimura, Joe; McKay, Caroline; McElwee, Newell E.; Choudhry, Niteesh K.

doi:10.1016/j.ahj.2017.09.019

Cited by 29 publications

(46 citation statements)

References 28 publications

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“…While we used pharmacy claims as the measure of medication adherence for this study, claims data may be unavailable to clinicians. [25] Franklin et al showed that electronic health record (EHR) data provide good predictions of medication adherence trajectories (i.e., statins, antihypertensives, and oral antidiabetic drugs) in a Medicare Advantage sample. [25] Ideally, multiple sources of medication adherence data would be integrated into the patient’s health record for enhanced clinical decision-making and improved patient outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…[25] Franklin et al showed that electronic health record (EHR) data provide good predictions of medication adherence trajectories (i.e., statins, antihypertensives, and oral antidiabetic drugs) in a Medicare Advantage sample. [25] Ideally, multiple sources of medication adherence data would be integrated into the patient’s health record for enhanced clinical decision-making and improved patient outcomes. [26] Regardless of the data source, longitudinal trajectories provide a clinically intuitive summary of a patient’s medication use patterns and can be created using already-collected data.…”

Section: Discussionmentioning

confidence: 99%

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Patterns of antihypertensive and statin adherence prior to dementia: findings from the adult changes in thought study

et al. 2019

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Background Detecting patients with undiagnosed dementia is an important clinical challenge. Changes in medication adherence might represent an early sign of cognitive impairment. We sought to examine antihypertensive and statin adherence trajectories in community-dwelling older adults, comparing people who went on to develop dementia to those who did not. Methods We analyzed data from Adult Changes in Thought (ACT), a population-based cohort study embedded within an integrated healthcare delivery system. Analyses included 4368 participants aged ≥65 years who had at least one follow-up visit. Research-quality dementia diagnoses were used to identify cases. We selected non-dementia control visits matched on age, sex, and study cohort that occurred at similar ACT follow-up time as the case’s dementia onset; we treated this as the index date. Participants were included if they were prevalent users of either a statin or antihypertensive medication on the first day of follow up – 3 years prior to the index date. Using prescription fill dates and days supply, we calculated daily binary medication availability measures for each participant (‘days covered’) over 3 years leading up to the index date. We used group-based trajectory models to identify patterns of antihypertensive and statin adherence, and used conditional logistic regression to examine associations between adherence trajectories and dementia. Results Four trajectories were identified for antihypertensive users (292 cases, 3890 control visits), including near perfect ( n = 1877, 36.6% cases, 45.5% controls), high ( n = 1840, 43.2% cases, 44.1% controls), moderate ( n = 365, 18.5% cases, 8.0% controls) and early poor adherence ( n = 100, 1.7% cases, 2.4% controls). Odds of dementia was 3 times greater for those with moderate antihypertensive adherence compared to those with near perfect adherence (adjusted OR 3.0, 95% CI 2.0, 4.3). Four trajectories were identified for statin users (148 cases, 1131 control visits), including high ( n = 1004, 75.0% cases, 79.0% controls), moderate ( n = 192, 19.6% cases, 14.4% controls), early poor ( n = 43, 2.0% cases, 3.5% controls), and delayed poor adherence ( n = 40, 3.4% cases, 3.1% controls). No association was detected between statin adherence trajectories and dementia. Conclusions Patterns of medication adherence may be useful to identify a subset of people at higher likelihood of developing dementia. Electronic supplementary material The online version of this article (10.1186/s12877-019-1058-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Patterns of antihypertensive and statin adherence prior to dementia: findings from the adult changes in thought study

et al. 2019

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“…22 In addition to direct querying about behavioral compliance history through interview, in-person assessment data are supplemented by comprehensive review of evidence from the medical record suggesting risk of behavioral non-compliance. 24 Such examples include a pervasive history of medication non-compliance, evidence of variable prescription refills, repeated documentation of compliance concerns across providers, higher rate of no-shows, "lost to follow-up" episodes and evidence of suboptimal medication management. 25 In addition, the majority of psychological and cognitive data assessed more formally below are obtained for their putative impact on ability to maintain optimal behavioral compliance.…”

Section: Behavioral Compliance and Substance Historymentioning

confidence: 99%

Psychosocial considerations in the assessment of hand transplant candidates: A single‐center experience and brief literature review

Smith

Potter

Manson³

et al. 2021

Clinical Transplantation

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Vascularized composite allograft, including hand transplantation (HT), has gained wider usage as a reconstructive treatment over the past 30 years. HT recipients face unique psychosocial challenges compared to their solid organ and/or bone marrow transplant counterparts. Accordingly, the psychosocial evaluation among HT candidates continues to evolve, leaving a lack of consensus as to the critical psychosocial domains and psychometric testing instruments to help evaluate individuals considering HT. The present manuscript describes the psychosocial evaluation process within the Duke HT program, which been contacted by 80 potential candidates since 2014.The Duke HT evaluation process incorporates a comprehensive psychosocial assessment within domains including personality, cognitive function, mood, behavioral adherence, social support, and substance use history, among others. Our experience underscores the potential utility of collecting thorough psychosocial evaluations, supplemented by psychometric test data, to comprehensively assess potential HT candidates.

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“…Most often, pharmacy records are used to measure adherence in terms of implementation and discontinuation [ 33 ]. EHR data have also been used for effective prediction of medication adherence trajectories [ 34 ], which has evoked certain discussions [ 35 ]. Menditto et al managed to integrate and analyze six databases from three countries, which allowed for a fair comparison of medication adherence across the various countries [ 36 ].…”

Section: Need For Standard Big Data–related Adherence Metrics For Resmentioning

confidence: 99%

The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint

Kardas¹,

Aguilar-Palacio²,

Almada³

et al. 2020

J Med Internet Res

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Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An important reason for this is the unprecedented demographic change at the beginning of the 21st century. Aging leads to multimorbidity and complex therapeutic regimens that create a fertile ground for nonadherence. As this scenario is a global problem, it needs a worldwide answer. Could this answer be provided, given the new opportunities created by the digitization of health care? Daily, health-related information is being collected in electronic health records, pharmacy dispensing databases, health insurance systems, and national health system records. These big data repositories offer a unique chance to study adherence both retrospectively and prospectively at the population level, as well as its related factors. In order to make full use of this opportunity, there is a need to develop standardized measures of adherence, which can be applied globally to big data and will inform scientific research, clinical practice, and public health. These standardized measures may also enable a better understanding of the relationship between adherence and clinical outcomes, and allow for fair benchmarking of the effectiveness and cost-effectiveness of adherence-targeting interventions. Unfortunately, despite this obvious need, such standards are still lacking. Therefore, the aim of this paper is to call for a consensus on global standards for measuring adherence with big data. More specifically, sound standards of formatting and analyzing big data are needed in order to assess, uniformly present, and compare patterns of medication adherence across studies. Wide use of these standards may improve adherence and make health care systems more effective and sustainable.

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The relative benefits of claims and electronic health record data for predicting medication adherence trajectory

Cited by 29 publications

References 28 publications

Patterns of antihypertensive and statin adherence prior to dementia: findings from the adult changes in thought study

Patterns of antihypertensive and statin adherence prior to dementia: findings from the adult changes in thought study

Psychosocial considerations in the assessment of hand transplant candidates: A single‐center experience and brief literature review

The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint

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