Validation of EHR medication fill data obtained through electronic linkage with pharmacies

Association of patient characteristics and insurance type with anti-obesity medications prescribing and fills

AIM

To characterize factors associated with the receipt of anti-obesity medication (AOM) prescription and fill.

MATERIALS AND METHODS

This retrospective cohort study used electronic health records from 1 January 2015 to 30 June 2023, in a large health system in Ohio and Florida. Adults with a body mass index ≥30 kg/m2 who attended ≥1 weight-management programme or had an initial AOM prescription between 1 July 2015 and 31 December 2022, were included. The main measures were a prescription for an AOM (naltrexone-bupropion, orlistat, phentermine-topiramate, liraglutide 3.0 mg and semaglutide 2.4 mg) and an AOM fill during the study follow-up.

RESULTS

We identified 50 678 adults, with a mean body mass index of 38 ± 8 kg/m2 and follow-up of 4.7 ± 2.4 years. Only 8.0% of the cohort had AOM prescriptions and 4.4% had filled prescriptions. In the multivariable analyses, being a man, Black, Hispanic and other race/ethnicity (vs. White), Medicaid, traditional Medicare, Medicare Advantage, self-pay and other insurance types (vs. private insurance) and fourth quartile of the area deprivation index (vs. first quartile) were associated with lower odds of a new prescription. Hispanic ethnicity, being a man, Medicaid, traditional Medicare and Medicare Advantage insurance types, liraglutide and orlistat (vs. naltrexone-buproprion) were associated with lower odds of AOM fill, while phentermine-topiramate was associated with higher odds. Among privately insured individuals, the insurance carrier was associated with both the odds of AOM prescription and fill.

CONCLUSIONS

Significant disparities exist in access to AOM both at the prescribing stage and getting the prescription filled based on patient characteristics and insurance type.

The use of routinely collected healthcare records for outcome assessment in clinical trials: a UK perspective

The use of routinely collected electronic healthcare records (EHR) for outcome assessment in clinical trials has been described as a 'disruptive' new technique more than a decade ago. Despite this potential, significant methodological issues and regulatory barriers have hampered the progress in this area. This article discusses the key considerations that trialists should take into account when incorporating EHR into their trials. These include considerations of the clinical relevance of the outcome, data timeliness and quality, ethical and regulatory issues, and some practical considerations for clinical trials units. In addition, this article describes the benefits of using EHR which include cost, reduced trial burden for participants and staff, follow up efficiencies, and improved health economic evaluation procedures. We also describe the major regulatory and start up costs of using EHR in clinical trials. This article focuses on the UK specific EHR landscape in clinical trials and would help researchers and trials units considering the use of this method of outcome data collection in their next trial. If the issues described are mitigated, this method will be a formidable tool for conducting pragmatic clinical trials.

Electronic health record-enabled routine assessment of medication adherence after solid organ transplantation: the time is now

Medication nonadherence after solid organ transplantation is recognized as an important impediment to long-term graft survival. Yet, assessment of adherence is often not part of routine care. In this Personal Viewpoint, we call for the transplant community to consider implementing a systematic process to screen and assess medication adherence. We believe acceptable tools are available to support integrating adherence assessments into the electronic health record. Creating a standard assessment can be done efficiently and cost-effectively if we come together as a community. More importantly, such monitoring can improve outcomes and strengthen provider-patient relationships. We further discuss the practical challenges and potential rebuttals to our position.

Medication adherence and costs of medical care among patients with Parkinson's disease: an observational study using electronic medical records

BACKGROUND

Adherence to antiparkinsonian drugs (APDs) is critical for patients with Parkinson's disease (PD), for which medication is the main therapeutic strategy. Previous studies have focused on specific disorders in a single system when assessing clinical factors affecting adherence to PD treatment, and no international comparative data are available on the medical costs for Chinese patients with PD. The present study aimed to evaluate medication adherence and its associated factors among Chinese patients with PD using a systematic approach and to explore the impact of adequate medication adherence on direct medical costs.

METHODS

A retrospective analysis was conducted using the electronic medical records of patients with PD from a medical center in China. Patients with a minimum of two APD prescriptions from January 1, 2016 to August 15, 2018 were included. Medication possession ratio (MPR) and proportion of days covered were used to measure APD adherence. Multiple linear regression analysis was used to identify factors affecting APD adherence. Gamma regression analysis was used to explore the impact of APD adherence on direct medical costs.

RESULTS

In total, 1,712 patients were included in the study, and the mean MPR was 0.68 (± 0.25). Increased number of APDs and all medications, and higher daily levodopa-equivalent doses resulted in higher MPR (mean difference [MD] = 0.04 [0.03-0.05]; MD = 0.02 [0.01-0.03]; MD = 0.03 [0.01-0.04], respectively); combined digestive system diseases, epilepsy, or older age resulted in lower MPR (MD = -0.06 [-0.09 to -0.03]; MD = -0.07 [-0.14 to -0.01]; MD = -0.02 [-0.03 to -0.01], respectively). Higher APD adherence resulted in higher direct medical costs, including APD and other outpatient costs. For a 0.3 increase in MPR, the two costs increased by $34.42 ($25.43-$43.41) and $14.63 ($4.86-$24.39) per year, respectively.

CONCLUSIONS

APD adherence rate among Chinese patients with PD was moderate and related primarily to age, comorbidities, and healthcare costs. The factors should be considered when prescribing APDs.

Early- and later-stage persistence with antiobesity medications: A retrospective cohort study

OBJECTIVE

The study's objective was to examine the percentage of patients with an initial antiobesity medication (AOM) fill who were persistent with AOM at 3, 6, and 12 months and to characterize factors associated with persistence at 12 months.

METHODS

This retrospective cohort study used electronic health records from January 2015 to July 2023 in a large health system in Ohio and Florida and included adults with BMI ≥30 kg/m2 who had an initial AOM prescription filled between 2015 and 2022.

RESULTS

The authors identified 1911 patients with a median baseline BMI of 38 (IQR, 34-44). Over time, 44% were persistent with AOM at 3 months, 33% at 6 months, and 19% at 12 months. Across categories of AOM, the highest 1-year persistence was in patients receiving semaglutide (40%). Semaglutide (adjusted odds ratio [AOR] = 4.26, 95% CI: 3.04-6.05) was associated with higher odds of 1-year persistence, and naltrexone-bupropion (AOR = 0.68, 95% CI: 0.46-1.00) was associated with lower odds, compared with phentermine-topiramate. Among patients who were persistent at 6 months, a 1% increase in weight loss at 6 months was associated with 6% increased odds of persistence at year 1 (AOR = 1.06, 95% CI: 1.03-1.09).

CONCLUSIONS

Later-stage persistence with AOM varies considerably based on the drug and the weight loss at 6 months.

Effect of an Intensive Food-as-Medicine Program on Health and Health Care Use: A Randomized Clinical Trial

Importance

Food-as-medicine programs are becoming increasingly common, and rigorous evidence is needed regarding their effects on health.

Objective

To test whether an intensive food-as-medicine program for patients with diabetes and food insecurity improves glycemic control and affects health care use.

Design, Setting, and Participants

This stratified randomized clinical trial using a wait list design was conducted from April 19, 2019, to September 16, 2022, with patients followed up for 1 year. Patients were randomly assigned to either participate in the program immediately (treatment group) or 6 months later (control group). The trial took place at 2 sites, 1 rural and 1 urban, of a large, integrated health system in the mid-Atlantic region of the US. Eligibility required a diagnosis of type 2 diabetes, a hemoglobin A1c (HbA1c) level of 8% or higher, food insecurity, and residence within the service area of the participating clinics.

Intervention

The comprehensive program provided healthy groceries for 10 meals per week for an entire household, plus dietitian consultations, nurse evaluations, health coaching, and diabetes education. The program duration was typically 1 year.

Main Outcomes and Measures

The primary outcome was HbA1c level at 6 months. Secondary outcomes included other biometric measures, health care use, and self-reported diet and healthy behaviors, at both 6 months and 12 months.

Results

Of 3712 patients assessed for eligibility, 3168 were contacted, 1064 were deemed eligible, 500 consented to participate and were randomized, and 465 (mean [SD] age, 54.6 [11.8] years; 255 [54.8%] female) completed the study. Of those patients, 349 (mean [SD] age, 55.4 [11.2] years; 187 [53.6%] female) had laboratory test results at 6 months after enrollment. Both the treatment (n = 170) and control (n = 179) groups experienced a substantial decline in HbA1c levels at 6 months, resulting in a nonsignificant, between-group adjusted mean difference in HbA1c levels of -0.10 (95% CI, -0.46 to 0.25; P = .57). Access to the program increased preventive health care, including more mean (SD) dietitian visits (2.7 [1.8] vs 0.6 [1.3] visits in the treatment and control groups, respectively), patients with active prescription drug orders for metformin (134 [58.26] vs 119 [50.64]) and glucagon-like peptide 1 medications (114 [49.56] vs 83 [35.32]), and participants reporting an improved diet from 1 year earlier (153 of 164 [93.3%] vs 132 of 171 [77.2%]).

Conclusions and Relevance

In this randomized clinical trial, an intensive food-as-medicine program increased engagement with preventive health care but did not improve glycemic control compared with usual care among adult participants. Programs targeted to individuals with elevated biomarkers require a control group to demonstrate effectiveness to account for improvements that occur without the intervention. Additional research is needed to design food-as-medicine programs that improve health.

Trial Registration

ClinicalTrials.gov Identifier: NCT03718832.

Neighborhood-Level Socioeconomic Status and Prescription Fill Patterns Among Patients With Heart Failure

Importance

Medication nonadherence is common among patients with heart failure with reduced ejection fraction (HFrEF) and can lead to increased hospitalization and mortality. Patients living in socioeconomically disadvantaged areas may be at greater risk for medication nonadherence due to barriers such as lower access to transportation or pharmacies.

Objective

To examine the association between neighborhood-level socioeconomic status (nSES) and medication nonadherence among patients with HFrEF and to assess the mediating roles of access to transportation, walkability, and pharmacy density.

Design, Setting, and Participants

This retrospective cohort study was conducted between June 30, 2020, and December 31, 2021, at a large health system based primarily in New York City and surrounding areas. Adult patients with a diagnosis of HF, reduced EF on echocardiogram, and a prescription of at least 1 guideline-directed medical therapy (GDMT) for HFrEF were included.

Exposure

Patient addresses were geocoded, and nSES was calculated using the Agency for Healthcare Research and Quality SES index, which combines census-tract level measures of poverty, rent burden, unemployment, crowding, home value, and education, with higher values indicating higher nSES.

Main Outcomes and Measures

Medication nonadherence was obtained through linkage of health record prescription data with pharmacy fill data and was defined as proportion of days covered (PDC) of less than 80% over 6 months, averaged across GDMT medications.

Results

Among 6247 patients, the mean (SD) age was 73 (14) years, and majority were male (4340 [69.5%]). There were 1011 (16.2%) Black participants, 735 (11.8%) Hispanic/Latinx participants, and 3929 (62.9%) White participants. Patients in lower nSES areas had higher rates of nonadherence, ranging from 51.7% in the lowest quartile (731 of 1086 participants) to 40.0% in the highest quartile (563 of 1086 participants) (P < .001). In adjusted analysis, patients living in the lower 2 nSES quartiles had significantly higher odds of nonadherence when compared with patients living in the highest nSES quartile (quartile 1: odds ratio [OR], 1.57 [95% CI, 1.35-1.83]; quartile 2: OR, 1.35 [95% CI, 1.16-1.56]). No mediation by access to transportation and pharmacy density was found, but a small amount of mediation by neighborhood walkability was observed.

Conclusions and Relevance

In this retrospective cohort study of patients with HFrEF, living in a lower nSES area was associated with higher rates of GDMT nonadherence. These findings highlight the importance of considering neighborhood-level disparities when developing approaches to improve medication adherence.

Cohort profile: a large EHR-based cohort with linked pharmacy refill and neighbourhood social determinants of health data to assess heart failure medication adherence

PURPOSE

Clinic-based or community-based interventions can improve adherence to guideline-directed medication therapies (GDMTs) among patients with heart failure (HF). However, opportunities for such interventions are frequently missed, as providers may be unable to recognise risk patterns for medication non-adherence. Machine learning algorithms can help in identifying patients with high likelihood of non-adherence. While a number of multilevel factors influence adherence, prior models predicting non-adherence have been limited by data availability. We have established an electronic health record (EHR)-based cohort with comprehensive data elements from multiple sources to improve on existing models. We linked EHR data with pharmacy refill data for real-time incorporation of prescription fills and with social determinants data to incorporate neighbourhood factors.

PARTICIPANTS

Patients seen at a large health system in New York City (NYC), who were >18 years old with diagnosis of HF or reduced ejection fraction (<40%) since 2017, had at least one clinical encounter between 1 April 2021 and 31 October 2022 and active prescriptions for any of the four GDMTs (beta-blocker, ACEi/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), mineralocorticoid receptor antagonist (MRA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i)) during the study period. Patients with non-geocodable address or outside the continental USA were excluded.

FINDINGS TO DATE

Among 39 963 patients in the cohort, the average age was 73±14 years old, 44% were female and 48% were current/former smokers. The common comorbid conditions were hypertension (77%), cardiac arrhythmias (56%), obesity (33%) and valvular disease (33%). During the study period, 33 606 (84%) patients had an active prescription of beta blocker, 32 626 (82%) had ACEi/ARB/ARNI, 11 611 (29%) MRA and 7472 (19%) SGLT2i. Ninety-nine per cent were from urban metropolitan areas.

FUTURE PLANS

We will use the established cohort to develop a machine learning model to predict medication adherence, and to support ancillary studies assessing associates of adherence. For external validation, we will include data from an additional hospital system in NYC.

Leveraging Electronic Health Record Technology and Team Care to Address Medication Adherence: Protocol for a Cluster Randomized Controlled Trial

BACKGROUND

Low 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.

OBJECTIVE

This 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.

METHODS

TEAMLET 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.

RESULTS

As 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.

CONCLUSIONS

The 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 REGISTRATION

ClinicalTrials.gov NCT05349422; https://clinicaltrials.gov/ct2/show/NCT05349422.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/47930.

Self-reported adherence and reasons for nonadherence among patients with low proportion of days covered for antihypertension medications

BACKGROUND: Incorporation of pharmacy fill data into the electronic health record has enabled calculations of medication adherence, as measured by proportion of days covered (PDC), to be displayed to clinicians. Although PDC values help identify patients who may be nonadherent to their medications, it does not provide information on the reasons for medication-taking behaviors. OBJECTIVE: To characterize self-reported adherence status to antihypertensive medications among patients with low refill medication adherence. Our secondary objective was to identify the most common reasons for nonadherence and examine the patient sociodemographic characteristics associated with these barriers. METHODS: Participants were adult patients seen in primary care clinics of a large, urban health system and on antihypertensive therapy with a PDC of less than 80% based on 6-month linked electronic health record-pharmacy fill data. We administered a validated medication adherence screener and a survey assessing reasons for antihypertensive medication nonadherence. We used descriptive statistics to characterize these data and logistic and Poisson regression models to assess the relationship between sociodemographic characteristics and adherence barriers. RESULTS: The survey was completed by 242 patients (57% female; 61.2% White; 79.8% not Latino/a or Hispanic). Of these patients, 45% reported missing doses of their medications in the last 7 days. In addition, 48% endorsed having at least 1 barrier to adherence and 38.4% endorsed 2 or more barriers. The most common barriers were being busy and having difficulty remembering to take medications. Compared with White participants, Black participants (incident rate ratio = 2.49; 95% CI = 1.93-3.22) and participants of other races (incident rate ratio = 2.16; 95% CI = 1.62-2.89) experienced a greater number of barriers. CONCLUSIONS: Nearly half of patients with low PDC reported nonadherence in the prior week, suggesting PDC can be used as a screening tool. Augmenting PDC with brief self-report tools can provide insights into the reasons for nonadherence. DISCLOSURES: Dr Kharmats, Ms Martinez, Dr Belli, Ms Zhao, Dr Mann, Dr Schoenthaler, and Dr Blecker received grants from the National Institute of Health/National Heart, Lung, Blood Institute. Dr Voils holds a license by Duke University for the DOSE-Nonadherence measure and is a consultant for New York University Grossman School of Medicine. This research was supported by the NIH (R01HL156355). Dr Kharmats received a postdoctoral training grant from the National Institutes of Health (5T32HL129953-04). Dr Voils was supported by a Research Career Scientist award from the Health Services Research & Development Service of the Department of Veterans Affairs (RCS 14-443). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the United States Government.

Comparison of Pharmacy Refill Data With Chemical Adherence Testing in Assessing Medication Nonadherence in a Safety Net Hospital Setting

Background Pharmacy fill data are a practical tool for assessing medication nonadherence. However, previous studies have not compared the accuracy of pharmacy fill data to measurement of plasma drug levels, or chemical adherence testing (CAT). Methods and Results We performed a cross-sectional study in patients with uncontrolled hypertension in outpatient clinics in a safety net health system. Plasma samples were obtained for measurement of common cardiovascular drugs, including calcium channel blockers, thiazide diuretics, beta blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and statins, using liquid chromatography mass spectrometry. Proportion of days covered (PDC), a method for tracking pharmacy fill data, was calculated via linkages with Surescripts, and its diagnostic test characteristics were compared with CAT. Among 77 patients with uncontrolled hypertension, 13 (17%) were nonadherent to at least 1 antihypertensive drug and 23 (37%) were nonadherent to statins by CAT. PDC was significantly lower in the nonadherent versus the adherent group by CAT only among patients prescribed an angiotensin-converting enzyme inhibitor/angiotensin receptor blocker or statin (all P<0.05) but not in patients prescribed other drug classes. The sensitivity and specificity of PDC in detecting nonadherence to angiotensin-converting enzyme inhibitors/angiotensin receptor blockers and statin drugs by CAT were 75% to 82% and 56% to 79%, respectively. The positive predictive value of PDC in detecting nonadherence was only 11% to 27% for antihypertensive drugs and 45% for statins. Conclusions PDC is useful in detecting nonadherence to angiotensin-converting enzyme inhibitors/angiotensin receptor blockers and statins but has limited usefulness in detecting nonadherence to calcium channel blockers, beta blockers, or thiazide diuretics and has a low positive predictive value for all drug classes.