A 3-month period of electronic monitoring can provide important information to the healthcare team to assess adherence and improve asthma control.
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ABSTRACT:
SUBMITTER: Jochmann A
PROVIDER: S-EPMC8311129 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
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A 3-month period of electronic monitoring can provide important information to the healthcare team to assess adherence and improve asthma control.
Jochmann Anja A Artusio Luca L Usemann Jakob J Jamalzadeh Angela A Bush Andrew A Frey Urs U Fleming Louise J LJ
ERJ open research 20210726 3
<b>In children with difficult asthma, a single period of electronic monitoring can help to assess a patient's adherence and the possible impact of improved adherence on asthma control</b> https://bit.ly/3c3Gj6n. ...[more]
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Project description:BackgroundInhaled corticosteroid (ICS) nonadherence is common among patients with asthma; however, interventions to improve adherence have often been complex and not easily applied to large patient populations.ObjectiveTo assess the effect of supplying patient adherence information to primary care providers.MethodsPatients and providers were members of a health system serving southeast Michigan. Providers (88 intervention; 105 control) and patients (1335 intervention; 1363 control) were randomized together by practice. Patients were age 5 to 56 years, had a diagnosis of asthma, and had existing prescriptions for ICS medication. Adherence was estimated by using prescription and fill data. Unlike clinicians in the control arm, intervention arm providers could view updated ICS adherence information on their patients via electronic prescription software, and further details on patient ICS use could be viewed by selecting that option. The primary outcome was ICS adherence in last 3 months of the study period.ResultsAt the study end for the intention-to-treat analysis, ICS adherence was not different among patients in the intervention arm compared with those in the control arm (21.3% vs 23.3%, respectively; P = .553). However, adherence was significantly higher among patients whose clinician elected to view their detailed adherence information (35.7%) compared with both control arm patients (P = .026) and intervention arm patients whose provider did not view adherence data (P = .002).ConclusionsOverall, providing adherence information to clinicians did not improve ICS use among patients with asthma. However, patient use may improve when clinicians are sufficiently interested in adherence to view the details of this medication use.
| S-EPMC2917519 | biostudies-literature
Project description:BackgroundClinicians' asthma guideline adherence in asthma care is suboptimal. The effort to improve adherence can be enhanced by assessing and monitoring clinicians' adherence to guidelines reflected in electronic health records (EHRs), which require costly manual chart review because many care elements cannot be identified by structured data.ObjectiveThis study was designed to demonstrate the feasibility of an artificial intelligence tool using natural language processing (NLP) leveraging the free text EHRs of pediatric patients to extract key components of the 2007 National Asthma Education and Prevention Program guidelines.MethodsThis is a retrospective cross-sectional study using a birth cohort with a diagnosis of asthma at Mayo Clinic between 2003 and 2016. We used 1,039 clinical notes with an asthma diagnosis from a random sample of 300 patients. Rule-based NLP algorithms were developed to identify asthma guideline-congruent elements by examining care description in EHR free text.ResultsNatural language processing algorithms demonstrated a sensitivity (0.82-1.0), specificity (0.95-1.0), positive predictive value (0.86-1.0), and negative predictive value (0.92-1.0) against manual chart review for asthma guideline-congruent elements. Assessing medication compliance and inhaler technique assessment were the most challenging elements to assess because of the complexity and wide variety of descriptions.ConclusionsNatural language processing technologies may enable the automated assessment of clinicians' documentation in EHRs regarding adherence to asthma guidelines and can be a useful population management and research tool to assess and monitor asthma care quality. Multisite studies with a larger sample size are needed to assess the generalizability of these NLP algorithms.
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Project description:The effect of inhaled corticosteroids (ICS) on the airway microbiome requires longitudinal research for corroboration. Asthma patients, not undergoing ICS treatment (baseline), were enrolled and prescribed ICS; all these patients were followed up with regular visits at 3 months (visit 1) and 9 months (visit 2). Induced sputum was collected, and fungal microbiota (mycobiome) and bacterial microbiota (bacteriome) were estimated using 16S rRNA and internal transcribed spacer (ITS) sequencing. Bacterial α diversity indices were not significantly different between baseline, visit 1, and visit 2. Visit 1 showed lower fungal evenness than the baseline, and visit 2 showed lower fungal diversity and evenness than the baseline. Fungal, but not bacterial, community compositions differed significantly between the baseline, visit 1, and visit 2. The most abundant bacterial phyla and genera did not differ significantly between the baseline, visit 1, and visit 2. Compared with the baseline, visit 1 showed significantly increased frequency of the fungal phylum Ascomycota and lower frequency of Basidiomycota. We found sharply decreased fungal genera Wallemia, Cladosporium, Penicillium, and Alternaria at visit 1 and visit 2 compared with the baseline, although the differences were not statistically significant. We also found the proportion of Basidiomycota was positively correlated with percentages of sputum eosinophils and neutrophils. The proportions of Saccharomyces, Wallemia, and Aplosporella were positively correlated with percentage of sputum eosinophils. Moreover, we identified distinct inter- and intra-kingdom interactions in baseline, visit 1, and visit 2. Therefore, ICS use altered the airway microbial diversity, evenness, community composition, and microbial connections.
| S-EPMC9283747 | biostudies-literature
Project description:IntroductionAdherence monitoring to inhaled corticosteroids is an essential component of asthma management. Electronic monitoring devices (EMD) provide objective data on date, time and number of actuations. However, most give no information on inhalation. Novel EMD (NEMD) platforms have the potential to monitor both activation and inhalation.AimTo assess the feasibility of NEMDs, in terms of usability, acceptability to patients and healthcare professionals and accuracy.MethodsThis was an open-label, prospective, mixed-methods, pragmatic randomised study. Children with asthma attending specialist tertiary care were randomised to one of four NEMD: Remote Directly Observed Therapy (R-DOT), Hailie Smartinhaler, INhaler Compliance Assessment device (INCA) and the Rafi-tone App. Following monitoring, participants were invited to focus groups or one-to-one interviews. Usability and acceptability were evaluated using themes identified from the focus groups and interviews. Adherence accuracy was determined using adherence data from each NEMD.ResultsThirty-five children were recruited; 18 (51%), (11 males, median age 13.5 (7-16) years) completed monitoring, 14 (78%) provided feedback. Participants identified various features such as ease of use and minimal effort as desirable criteria for an NEMD. The Hailie and INCA fulfilled these criteria and were able to record both actuation and inhalation. Negative themes included a 'Big Brother' effect and costs.ConclusionThere was no 'one size fits all', as participants identified advantages and disadvantages for each NEMD. Devices that can easily calculate adherence to activation and inhalation have the potential to have greatest utility in clinical practice. Each NEMD has different functionality and therefore choice of platform should be determined by the needs of the patient and healthcare professional.
| S-EPMC7646352 | biostudies-literature
Project description:Deep neural networks have been proven effective in classifying human interactions into emotions, especially by encoding multiple input modalities. In this work, we assess the robustness of a transformer-based multimodal audio-text classifier for emotion recognition, by perturbing the input at inference time using attacks which we design specifically to corrupt information deemed important for emotion recognition. To measure the impact of the attacks on the classifier, we compare between the accuracy of the classifier on the perturbed input and on the original, unperturbed input. Our results show that the multimodal classifier is more resilient to perturbation attacks than the equivalent unimodal classifiers, suggesting that the two modalities are encoded in a way that allows the classifier to benefit from one modality even when the other one is slightly damaged.
| S-EPMC10075078 | biostudies-literature
Project description:Infrequent use of inhaled corticosteroids (ICS) and/or over-reliance of short-acting β-agonists (SABA) are recognised as key contributors to increased morbidity and mortality in asthma. The most frequent measures of ICS adherence and SABA use rely on patient-reported questionnaires or prescription refill records, neither of which are considered sufficiently reliable. Technological advancements in the development of electronic monitoring of inhaler devices allow for monitoring of use, as well as recording of and feedback on inhaler technique for some devices. Most electronic monitoring devices (EMDs) are paired with a smartphone application, allowing patients to set reminders and display both preventer and reliever use over time. This allows identification of intentional and unintentional ICS non-adherence as well as frequency of SABA use. This information assists clinicians in distinguishing difficult-to-control from severe asthma. Although additional evidence is required to assess the impact of EMDs on clinical outcome measures such as exacerbation rate, the introduction of EMDs into the asthma armoury is a significant step forward in asthma care with the potential to improve asthma-related outcomes.
| S-EPMC9488666 | biostudies-literature
Project description:BackgroundRespiratory Medication Therapy Adherence Clinic (RMTAC) is an initiative by the Ministry of Health (MOH) Malaysia to improve patients' medication adherence, as an adjunct to the usual physician care (UC). This study aimed to evaluate the cost-effectiveness of combined strategy of RMTAC and UC (RMTAC + UC) vs. UC alone in asthma patients, from the MOH Malaysia perspective.MethodsA lifetime horizon dynamic adherence Markov model with monthly cycle was developed, for quality-adjusted life year (QALY) gained and hospitalization averted outcomes. Transition probabilities of composite asthma control and medication adherence, utilities, costs, and mortality rates due to all causes were measured from local data sources. Effectiveness, exacerbation rates, and asthma mortality rates were taken from non-local data sources. One-way sensitivity analysis (SA) was conducted for assessing parameter uncertainties, whereas probabilistic SA (PSA) was conducted on a different set of utilities and effectiveness data. Costs were adjusted to 2014 US dollars ($). Both costs and benefits were discounted at a 3% rate annually.ResultsRMTAC + UC was found to be a dominant alternative compared to UC alone; $- 13,639.40 ($- 109,556.90 to $104,445.54) per QALY gained and $- 428.93 ($- 521.27 to ($- 328.69)) per hospitalization averted. These results were found to be robust against changes in all parameters except utilities in the one-way SA, and for both scenarios in PSA.ConclusionsRMTAC + UC is more effective and yet cheaper than UC alone, from the MOH perspective. For the benefit of both MOH and patients, RMTAC is thus recommended to be remained, and expanded to more healthcare settings where possible.
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Project description:PurposeThe purpose of this study was to assess whether providing medication adherence information with or without motivational interviewing improves diabetes and lipid control.MethodsStudy participants were adult members of a health system in southeast Michigan, were using both oral diabetes and lipid-lowering medications, and had glycated hemoglobin (A1C) or low-density lipoprotein cholesterol (LDL-C) levels not at goal. Participants were randomly assigned to receive usual care (UC), n = 567; have medication adherence information (AI) provided to their physician, n = 569; or have AI and receive motivational interviewing (MI) though trained staff (AI + MI), n = 556. Primary outcomes were A1C and LDL-C levels at 18 months post randomization.ResultsPrimary outcomes were not significantly different between patients in the AI or AI + MI study arms when compared with UC. Similarly, neither oral diabetes nor lipid-lowering medication adherence was significantly different between groups. Patient participation in the AI + MI arm was low and limit the interpretation of the study results, but post hoc analysis of the AI + MI study arm showed that the number of MI sessions received was positively associated with only oral diabetes medication adherence.ConclusionNeither AI nor MI significantly improved diabetes and lipid control when compared with UC. Moreover, patient participation appeared to be a particular barrier for MI.
| S-EPMC4722813 | biostudies-literature
Project description:BackgroundLow medication adherence is a common cause of high blood pressure but is often unrecognized in clinical practice. Electronic data linkages between electronic health records (EHRs) and pharmacies offer the opportunity to identify low medication adherence, which can be used for interventions at the point of care. We developed a multicomponent intervention that uses linked EHR and pharmacy data to automatically identify patients with elevated blood pressure and low medication adherence. The intervention then combines team-based care with EHR-based workflows to address medication nonadherence.ObjectiveThis study aims to describe the design of the Leveraging EHR Technology and Team Care to Address Medication Adherence (TEAMLET) trial, which tests the effectiveness of a multicomponent intervention that leverages EHR-based data and team-based care on medication adherence among patients with hypertension.MethodsTEAMLET is a pragmatic, cluster randomized controlled trial in which 10 primary care practices will be randomized 1:1 to the multicomponent intervention or usual care. We will include all patients with hypertension and low medication adherence who are seen at enrolled practices. The primary outcome is medication adherence, as measured by the proportion of days covered, and the secondary outcome is clinic systolic blood pressure. We will also assess intervention implementation, including adoption, acceptability, fidelity, cost, and sustainability.ResultsAs of May 2023, we have randomized 10 primary care practices into the study, with 5 practices assigned to each arm of the trial. The enrollment for the study commenced on October 5, 2022, and the trial is currently ongoing. We anticipate patient recruitment to go through the fall of 2023 and the primary outcomes to be assessed in the fall of 2024.ConclusionsThe TEAMLET trial will evaluate the effectiveness of a multicomponent intervention that leverages EHR-based data and team-based care on medication adherence. If successful, the intervention could offer a scalable approach to address inadequate blood pressure control among millions of patients with hypertension.Trial registrationClinicalTrials.gov NCT05349422; https://clinicaltrials.gov/ct2/show/NCT05349422.International registered report identifier (irrid)DERR1-10.2196/47930.
| S-EPMC10362494 | biostudies-literature
Project description:BackgroundMedication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case. Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study.MethodsOver 1.6 million asthma controllers were prescribed for our cohort of 91,334 individuals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared.ResultsEstimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures.ConclusionsWe evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.
| S-EPMC10337150 | biostudies-literature