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Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring.

ABSTRACT: Poor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy.We validated a liquid chromatography-tandem mass spectrometry assay to detect and/or quantify 38 medications across a broad range of chronic diseases to obtain a comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults, we found that 46% of medications assessed were detected in patients as prescribed in the medical record. Of the remaining medications, 23% were detected, but not listed in the medical record while 30% were prescribed to patients, but not detected in blood. To determine how often each detected medication fell within literature-derived reference ranges when taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent patients. In this cohort, we found that 53% of medications that were taken as prescribed, as determined using patient self-reporting, were not within the blood reference range. Of the medications not in range, 83% were below and 17% above the lower and upper range limits, respectively. Only 32% of out-of-range medications could be attributed to short oral half-lives, leaving extensive exposure variability to result from patient behavior, undefined drug interactions, genetics, and other characteristics that can affect medication exposure.This is the first study to assess compliance, medical record accuracy, and exposure as determinants of real-world treatment and response. Variation in medication detection and exposure is greater than previously demonstrated, illustrating the scope of current therapy issues and opening avenues that warrant further investigation to optimize medication therapy.

SUBMITTER: Ryan TP

PROVIDER: S-EPMC5619774 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring.

Ryan Timothy P TP Morrison Ryan D RD Sutherland Jeffrey J JJ Milne Stephen B SB Ryan Kendall A KA Daniels J Scott JS Misra-Hebert Anita A Hicks J Kevin JK Vogan Eric E Teng Kathryn K Daly Thomas M TM

PloS one 20170928 9

<h4>Background</h4>Poor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy.<h4>Methods and findings</h4>We validated a liquid chromatography-tandem mass spectrometry as ...[more]

PMID: 28957369

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Project description:Importance:Inaccurate medication records and poor medication adherence result in incomplete knowledge of therapy for patients. Objective:To study accuracy of medical records and patient adherence by measuring blood concentrations of medications. Design, Setting, and Participants:This cross-sectional study validated a serum-based liquid chromatography-tandem mass spectrometry assay to simultaneously quantify 263 medications used for acute and chronic conditions. The assay panel was applied to 3 clinical patient cohorts: residual serum from 1000 randomly selected samples sent for routine clinical chemistry testing between April 8 and October 6, 2015 (residuals cohort), 50 prospectively enrolled patients in a gastroenterology clinic between March 1 and March 15, 2016, who were prescribed more than 5 medications (gastroenterology care cohort), and a convenience cohort of 296 patients with hypertension who sought care in an emergency department (ED care cohort) between July 1, 2012, and April 25, 2013. Integrated data analysis of the cohorts was performed from August 22 to November 29, 2017. Main Outcomes and Measures:Medication serum concentrations, electronic health record medication lists, and predicted drug interactions. Results:Of the 1346 total samples, 1000 came from the residuals cohort (640 women and 360 men; median age, 60 years [interquartile range (IQR), 44-71 years]), 50 from the gastroenterology care cohort (30 women and 20 men; median age, 66 years [IQR, 62-70 years]), and 296 from the ED care cohort (160 women and 136 men; median age, 59 years [IQR, 52-66 years]). Median medication adherence, defined as the subset of detected medications from the prescription record, was 83% (IQR, 50%-100%) in the residuals cohort, 100% (IQR, 84%-100%) in the gastroenterology care cohort, and 78% (IQR, 57%-100%) in the ED care cohort. Patients adherent to 1 medication were more often adherent to other medications. Among patients prescribed 3 medications or more, there were no significant associations between medication adherence and sex or number of prescribed medications, and there was a modest association between adherence and age. By comparing detected vs prescribed medications, we detected a median of 0 (IQR, 0-2) medications per patient that were not listed in the electronic health record in the residuals cohort, 1 (IQR, 0-2) medication per patient that was not listed in the electronic health record in the gastroenterology care cohort, and 1 (IQR, 0-2) medication per patient that was not listed in the electronic health record in the ED care cohort. A total of 435 patients (43.5%) in the residuals cohort had no discrepancy between the electronic health record and detected medication lists, 22 patients (44.0%) in the gastroenterology care cohort had no discrepancy between the electronic health record and detected medication lists, and 41 patients (13.9%) in the ED care cohort had no discrepancy between the electronic health record and detected medication lists. Half of adverse drug reaction alerts occurred among medications detected without prescription. Conclusions and Relevance:Comprehensive medication monitoring offers promise to improve adherence, the accuracy of medical records, and the safety for patients with polypharmacy.

Project description:ObjectivesWe tried to clarify, by using representative national data in a real-world setting, whether single-pill combinations (SPCs) of antihypertensives actually improve medication adherence.DesignA nationwide population-based study.SettingWe used a 2.2% cohort (n=1 048 061) of the total population (n=46 605 433) that was randomly extracted by National Health Insurance of Korea from 2008 to 2013.ParticipantsWe included patients (n=116 677) who were prescribed with the same antihypertensive drugs for at least 1 year and divided them into groups of angiotensin II receptor blocker (ARB)-only, calcium channel blocker (CCB)-only, multiple-pill combinations (MPCs) and SPCs of ARB/CCB.Primary outcome measuresMedication possession ratio (MPR), a frequently used indirect measurement method of medication adherence.ResultsAdjusted MPR was higher in combination therapy (89.7% in SPC, 87.2% in MPC) than monotherapy (81.6% in ARB, 79.7% in CCB), and MPR of SPC (89.7%, 95% CI 89.3 to 90.0) was higher than MPR of MPC (87.2%, 95% CI 86.7 to 87.7) (p<0.05). In subgroup analysis, adherence of SPC and MPC was 92.3% (95% CI 91.5 to 93.0) vs 88.1% (95% CI 87.1 to 89.0) in those aged 65-74 years and 89.3% (95% CI 88.0 to 90.7) vs 84.8% (95% CI 83.3 to 92.0) in those ≥75 years (p<0.05). According to total pill numbers, adherence of SPC and MPC was 90.9% (CI 89.8 to 92.0) vs 85.3% (95% CI 84.1 to 86.5) in seven to eight pills and 91.2% (95% CI 89.3 to 93.1) vs 82.5% (95% CI 80.6 to 84.4) in nine or more (p<0.05). The adherence difference between SPC and MPC started to increase at five to six pills and at age 50-64 years (p<0.05). When analysed according to elderly status, the adherence difference started to increase at three to four pills in the elderly (≥65 years) and at five to six in the non-elderly group (20-64 years) (p<0.05). These differences all widened further with increasing age and the total medications.ConclusionSPC regimens demonstrated higher adherence than MPC, and this tendency is more pronounced with increasing age and the total number of medications.

Project description:Background: Poor medication adherence is a major challenge in asthma and objective assessment of inhaler adherence is needed. InspirerMundi app aims to monitor inhaler adherence while turning it into a positive experience through gamification and social support. Objective: We assessed the medium-term feasibility of the InspirerMundi app to monitor inhaler adherence in real-world patients with persistent asthma (treated with daily inhaled medication). In addition, we attempted to identify the characteristics of the patients related to higher app use. Methods: Two real-world multicenter observational studies, with one initial face-to-face visit and a 4-month telephone interview, were conducted in 29 secondary care centers from Portugal. During an initial face-to-face visit, patients were invited to use the app daily to register their asthma medication intakes. A scheduled intake was considered taken when patients took a photo of the medication (inhaler, blister, or others) using the image-based medication detection tool. Medication adherence was calculated as the number of doses taken as a percentage of the number scheduled. Interacting with the app ≥30 days was used as the cut-off for higher app use. Results: A total of 114 patients {median 20 [percentile 25 to percentile 75 (P25-P75) 16-36] years, 62% adults} were invited, 107 (94%) installed the app and 83 (73%) completed the 4-month interview. Patients interacted with the app for a median of 18 [3-45] days, translated on a median use rate of 15 [3-38]%. Median inhaler adherence assessed through the app was 34 [4-73]% when considering all scheduled inhalations for the study period. Inhaler adherence assessed was not significantly correlated with self-reported estimates. Median adherence for oral and other medication was 41 [6-83]% and 43 [3-73]%, respectively. Patients with higher app use were slightly older (p = 0.012), more frequently taking medication for other health conditions (p = 0.040), and more frequently prescribed long-acting muscarinic antagonists (LAMA, p = 0.024). After 4 months, Control of Allergic Rhinitis and Asthma Test (CARAT) scores improved (p < 0.001), but no differences between patients interacting with the app for 30 days or less were seen. Conclusions: The InspirerMundi app was feasible to monitor inhaler adherence in patients with persistent asthma. The persistent use of this mHealth technology varies widely. A better understanding of characteristics related to higher app use is still needed before effectiveness studies are undertaken.

Project description:BackgroundThe rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs.ObjectiveThe aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes.MethodsData were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality.ResultsThe EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS.ConclusionsIn this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities.

Dataset Information

Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring.

Publications

Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring.

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