Development of an algorithm to link electronic health record prescriptions with pharmacy dispense claims

Outpatient-Based Opioid Treatment Engagement and Attendance: A Prospective Cohort Study of Homeless-Experienced Adults

BACKGROUND

The opioid overdose epidemic disproportionately impacts people experiencing homelessness. Outpatient-based opioid treatment (OBOT) programs have been established in homeless health care settings across the USA, but little is known about the success of these programs in engaging and retaining this highly marginalized patient population in addiction care.

OBJECTIVE

To evaluate predictors of initial engagement and subsequent attendance in a homeless-tailored OBOT program.

DESIGN

Prospective cohort study with 4 months of follow-up.

PARTICIPANTS

A total of 148 homeless-experienced adults (≥18 years) who newly enrolled in the Boston Healthcare for the Homeless Program (BHCHP) OBOT program over a 1-year period (1/6/2022-1/5/2023).

MAIN MEASURES

The primary outcomes were (1) initial OBOT program engagement, defined as having ≥2 additional OBOT visits within 1 month of OBOT enrollment, and (2) subsequent OBOT program attendance, measured monthly from months 2 to 4 of follow-up.

KEY RESULTS

The average age was 41.7 years (SD 10.2); 23.6% were female, 35.8% were Hispanic, 12.8% were non-Hispanic Black, and 43.9% were non-Hispanic White. Over one-half (57.4%) were initially engaged. OBOT program attendances during months 2, 3, and 4 were 60.8%, 50.0%, and 41.2%, respectively. One-quarter (24.3%) were initially engaged and then attended the OBOT program every month during the follow-up period. Participants in housing or residential treatment programs (vs. unhoused; adjusted odds ratios (aORs) = 2.52; 95% CI = 1.17-5.44) and those who were already on or initiated a medication for opioid use disorder (OUD) (aOR = 6.53; 95% CI = 1.62-26.25) at the time of OBOT enrollment had higher odds of engagement. Older age (aOR = 1.74 per 10-year increment; 95% CI = 1.28-2.38) and initial engagement (aOR = 3.50; 95% CI = 1.86-6.59) conferred higher odds of attendance.

CONCLUSIONS

In this study, over half initially engaged with the OBOT program, with initial engagement emerging as a strong predictor of subsequent OBOT program attendance. Interventions aimed at enhancing initial OBOT program engagement, including those focused on housing and buprenorphine initiation, may improve longer-term outcomes in this marginalized population.

Automated identification of unstandardized medication data: a scalable and flexible data standardization pipeline using RxNorm on GEMINI multicenter hospital data

Objective

Patient data repositories often assemble medication data from multiple sources, necessitating standardization prior to analysis. We implemented and evaluated a medication standardization procedure for use with a wide range of pharmacy data inputs across all drug categories, which supports research queries at multiple levels of granularity.

Methods

The GEMINI-RxNorm system automates the use of multiple RxNorm tools in tandem with other datasets to identify drug concepts from pharmacy orders. GEMINI-RxNorm was used to process 2 090 155 pharmacy orders from 245 258 hospitalizations between 2010 and 2017 at 7 hospitals in Ontario, Canada. The GEMINI-RxNorm system matches drug-identifying information from pharmacy data (including free-text fields) to RxNorm concept identifiers. A user interface allows researchers to search for drug terms and returns the relevant original pharmacy data through the matched RxNorm concepts. Users can then manually validate the predicted matches and discard false positives. We designed the system to maximize recall (sensitivity) and enable excellent precision (positive predictive value) with efficient manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes.

Results

Manual coding was performed for 1 948 817 pharmacy orders and GEMINI-RxNorm successfully returned 1 941 389 (99.6%) orders. Recall was greater than 0.985 in all 13 drug classes, and the F1-score and precision remained above 0.90 in all drug classes, facilitating efficient manual review to achieve 100% precision. GEMINI-RxNorm saved time substantially compared with manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 to 5 s and reducing the number of rows needed to be reviewed by up to 99.99%.

Discussion and Conclusion

GEMINI-RxNorm presents a novel combination of RxNorm tools and other datasets to enable accurate, efficient, flexible, and scalable standardization of pharmacy data. By facilitating efficient manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.

Underprescribing vs underfilling to oral anticoagulation: An analysis of linked medical record and claims data for a nationwide sample of patients with atrial fibrillation

BACKGROUND: Oral anticoagulants (OAC) is indicated for stroke prevention in patients with atrial fibrillation (AF) with a moderate or high risk of stroke. Despite the benefits of stroke prevention, only 50%-60% of Americans with nonvalvular AF and a moderate or high risk of stroke receive OAC medication. OBJECTIVE: To understand the extent to which low OAC use by patients with AF is attributed to underprescribing or underfilling once the medication is prescribed. METHODS: This is a retrospective cohort study that used linked claims data and electronic health records from Optum Integrated data. Participants were adults (aged ≥ 18 years) with first AF between January 2013 and June 2017. The outcomes included (1) being prescribed OACs within 180 days of AF diagnosis or not and (2) filling an OAC prescription or not among patients with AF who were prescribed an OAC within 150 days of AF diagnosis. Multivariable logistic regression models were constructed to determine factors associated with underprescribing and underfilling. RESULTS: Of the 6,141 individuals in the study cohort, 51% were not prescribed OACs within 6 months of their AF diagnosis. Of the 2,956 patients who were prescribed, 19% did not fill it at the pharmacy. In the final adjusted model, younger age, location (Northeast and South), a low CHA₂DS₂-VASc score, and a high HAS-BLED score were associated with a lower likelihood of being prescribed OACs. Among patients who were prescribed, Medicare enrollment (odds ratio [OR] [95% CI] = 2.2 [1.3-3.7]) and having a direct oral anticoagulant prescription (1.5 [1.2-1.9]) were associated with a lower likelihood of filling the prescription. CONCLUSIONS: Both underprescribing and underfilling are major drivers of low OAC use among patients with AF, and solutions to increase OAC use must address both prescribing and filling. DISCLOSURES: Research reported in this study was supported by the National Heart, Lung and Blood Institute (K01HL142847 and R01HL157051). Dr Guo is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK133465), PhMRA Foundation Research Starter Award, and the University of Florida Research Opportunity Seed Fund. Dr Hernandez reports scientific advisory board fees from Pfizer and Bristol Myers Squibb, outside of the submitted work.

Digital Health Interventions to Enhance Prevention in Primary Care: Scoping Review

Background

Disease prevention is a central aspect of primary care practice and is comprised of primary (eg, vaccinations), secondary (eg, screenings), tertiary (eg, chronic condition monitoring), and quaternary (eg, prevention of overmedicalization) levels. Despite rapid digital transformation of primary care practices, digital health interventions (DHIs) in preventive care have yet to be systematically evaluated.

Objective

This review aimed to identify and describe the scope and use of current DHIs for preventive care in primary care settings.

Methods

A scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and Medical Subject Headings terms covering primary care professionals, prevention and care management, and digital health. A subgroup analysis identified relevant studies conducted in US primary care settings, excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (eg, health care performance and implementation science), and study quality as per Oxford levels of evidence were abstracted.

Results

The search yielded 5274 citations, of which 1060 full-text articles were identified. Following a subgroup analysis, 241 articles met the inclusion criteria. Studies primarily examined DHIs among health information technologies, including EHRs (166/241, 68.9%), clinical decision support (88/241, 36.5%), telehealth (88/241, 36.5%), and multiple technologies (154/241, 63.9%). DHIs were predominantly used for tertiary prevention (131/241, 54.4%). Of the core primary care functions, comprehensiveness was addressed most frequently (213/241, 88.4%). DHI users were providers (205/241, 85.1%), patients (111/241, 46.1%), or multiple types (89/241, 36.9%). Reported outcomes were primarily clinical (179/241, 70.1%), and statistically significant improvements were common (192/241, 79.7%). Results were summarized across the following 5 topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. The quality of the included studies was moderate to low.

Conclusions

Preventive DHIs in primary care settings demonstrated meaningful improvements in both clinical and nonclinical outcomes, and across user types; however, adoption and implementation in the US were limited primarily to EHR platforms, and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluations of negative results, effects on health disparities, and many other gaps remain to be explored.

Clinical risk factors and social needs of 30-day readmission among patients with diabetes: A retrospective study of the Deep South

Introduction

Evidence is needed for 30-day readmission risk factors (clinical factors and social needs) among patients with diabetes in the Deep South. To address this need, our objectives were to identify risk factors associated with 30-day readmissions among this population and determine the added predictive value of considering social needs.

Methods

This retrospective cohort study utilized electronic health records from an urban health system in the Southeastern U.S. The unit of analysis was index hospitalization with a 30-day washout period. The index hospitalizations were preceded by a 6-month pre-index period to capture risk factors (including social needs), and hospitalizations were followed 30 days post-discharge to evaluate all-cause readmissions (1=readmission; 0=no readmission). We performed unadjusted (chi-square and student's t-test, where applicable) and adjusted analyses (multiple logistic regression) to predict 30-day readmissions.

Results

A total of 26,332 adults were retained in the study population. Eligible patients contributed a total of 42,126 index hospitalizations, and the readmission rate was 15.21%. Risk factors associated with 30-day readmissions included demographics (e.g., age, race/ethnicity, insurance), characteristics of hospitalizations (e.g., admission type, discharge status, length of stay), labs and vitals (e.g., highest and lowest blood glucose measurements, systolic and diastolic blood pressure), co-existing chronic conditions, and preadmission antihyperglycemic medication use. In univariate analyses of social needs, activities of daily living (p<0.001), alcohol use (p<0.001), substance use (p=0.002), smoking/tobacco use (p<0.001), employment status (p<0.001), housing stability (p<0.001), and social support (p=0.043) were significantly associated with readmission status. In the sensitivity analysis, former alcohol use was significantly associated with higher odds of readmission compared to no alcohol use [aOR (95% CI): 1.121 (1.008-1.247)].

Conclusions

Clinical assessment of readmission risk in the Deep South should consider patients' demographics, characteristics of hospitalizations, labs, vitals, co-existing chronic conditions, preadmission antihyperglycemic medication use, and social need (i.e., former alcohol use). Factors associated with readmission risk can help pharmacists and other healthcare providers identify high-risk patient groups for all-cause 30-day readmissions during transitions of care. Further research is needed about the influence of social needs on readmissions among populations with diabetes to understand the potential clinical utility of incorporating social needs into clinical services.

Agreement in Medications Reported in Medical and Dental Electronic Health Records

INTRODUCTION

The historical separation between medicine and dentistry has resulted in the creation of separate health records, which have the potential to negatively impact patient care and safety. Of particular importance, errors or omissions in medication lists in separate electronic health records (EHRs) may lead to medical errors and serious adverse outcomes.

OBJECTIVE

This study aimed to compare medication lists reported in the EHRs of active patients treated by both the University of Michigan School of Dentistry and Michigan Medicine to determine if differences exist.

METHODS

In this cohort study, EHRs of a population of 159,733 patients that the University of Michigan medical and dental clinics share in common were investigated for agreement in the reporting of 16 medications. After exclusion of minors and patients not seen in the last 5 y, records of 27,277 patients were examined.

RESULTS

The maximum percentage of agreement in medications reported in both records was 52% for levothyroxine, and the minimum was 7% for sildenafil. The medical record had a significantly higher number of unique medications than the dental record, suggesting higher underreporting in the dental setting.

CONCLUSION

The lack of agreement in the report of medications with serious dental and medical implications argues in favor of unification of records and use of available technology to increase accurate medication reporting.

KNOWLEDGE TRANSFER STATEMENT

The results demonstrate a lack of agreement between medications reported in medical and dental records, which can have serious implications to patients' health. A unified health record, employing available technology to increase accurate medication reporting, would mitigate this problem.

Linkage of primary care prescribing records and pharmacy dispensing Records in the Salford Lung Study: application in asthma

BACKGROUND

Records of medication prescriptions can be used in conjunction with pharmacy dispensing records to investigate the incidence of adherence, which is defined as observing the treatment plans agreed between a patient and their clinician. Using prescribing records alone fails to identify primary non-adherence; medications not being collected from the dispensary. Using dispensing records alone means that cases of conditions that resolve and/or treatments that are discontinued will be unaccounted for. While using a linked prescribing and dispensing dataset to measure medication non-adherence is optimal, this linkage is not routinely conducted. Furthermore, without a unique common event identifier, linkage between these two datasets is not straightforward.

METHODS

We undertook a secondary analysis of the Salford Lung Study dataset. A novel probabilistic record linkage methodology was developed matching asthma medication pharmacy dispensing records and primary care prescribing records, using semantic (meaning) and syntactic (structure) harmonization, domain knowledge integration, and natural language feature extraction. Cox survival analysis was conducted to assess factors associated with the time to medication dispensing after the prescription was written. Finally, we used a simplified record linkage algorithm in which only identical records were matched, for a naïve benchmarking to compare against the results of our proposed methodology.

RESULTS

We matched 83% of pharmacy dispensing records to primary care prescribing records. Missing data were prevalent in the dispensing records which were not matched - approximately 60% for both medication strength and quantity. A naïve benchmarking approach, requiring perfect matching, identified one-quarter as many matching prescribing records as our methodology. Factors associated with delay (or failure) to collect the prescribed medication from a pharmacy included season, quantity of medication prescribed, previous dispensing history and class of medication. Our findings indicate that over 30% of prescriptions issued were not collected from a dispensary (primary non-adherence).

CONCLUSIONS

We have developed a probabilistic record linkage methodology matching a large percentage of pharmacy dispensing records with primary care prescribing records for asthma medications. This will allow researchers to link datasets in order to extract information about asthma medication non-adherence.

Network Engagement in Action: Stakeholder Engagement Activities to Enhance Patient-centeredness of Research

BACKGROUND

Stakeholders (ie, patients, policymakers, clinicians, advocacy groups, health system leaders, payers, and others) offer critical input at various stages in the research continuum, and their contributions are increasingly recognized as an important component of effective translational research. Natural experiments, in particular, may benefit from stakeholder feedback in addressing real-world issues and providing insight into future policy decisions, though best practices for the engagement of stakeholders in observational studies are limited in the literature.

METHODS

The Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) network utilizes rigorous methods to evaluate natural experiments in health policy and program delivery with a focus on diabetes-related outcomes. Each of the 8 partnering institutions incorporates stakeholder engagement throughout multiple study phases to enhance the patient-centeredness of results. NEXT-D2 dedicates a committee to Engagement for resource sharing, enhancing engagement approaches, and advancing network-wide engagement activities. Key stakeholder engagement activities include Study Meetings, Proposal Development, Trainings & Educational Opportunities, Data Analysis, and Results Dissemination. Network-wide patient-centered resources and multimedia have also been developed through the broad expertise of each site's stakeholder group.

CONCLUSIONS

This collaboration has created a continuous feedback loop wherein site-level engagement approaches are informed via the network and network-level engagement efforts are shaped by individual sites. Emerging best practices include: incorporating stakeholders in multiple ways throughout the research, building on previous relationships with stakeholders, enhancing capacity through stakeholder and investigator training, involving stakeholders in refining outcome choices and understanding the meaning of variables, and recognizing the power of stakeholders in maximizing dissemination.

Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records

PURPOSE

To predict the need for surgical intervention in patients with primary open-angle glaucoma (POAG) using systemic data in electronic health records (EHRs).

DESIGN

Development and evaluation of machine learning models.

METHODS

Structured EHR data of 385 POAG patients from a single academic institution were incorporated into models using multivariable logistic regression, random forests, and artificial neural networks. Leave-one-out cross-validation was performed. Mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Youden index were calculated for each model to evaluate performance. Systemic variables driving predictions were identified and interpreted.

RESULTS

Multivariable logistic regression was most effective at discriminating patients with progressive disease requiring surgery, with an AUC of 0.67. Higher mean systolic blood pressure was associated with significantly increased odds of needing glaucoma surgery (odds ratio [OR] = 1.09, P < .001). Ophthalmic medications (OR = 0.28, P < .001), non-opioid analgesic medications (OR = 0.21, P = .002), anti-hyperlipidemic medications (OR = 0.39, P = .004), macrolide antibiotics (OR = 0.40, P = .03), and calcium blockers (OR = 0.43, P = .03) were associated with decreased odds of needing glaucoma surgery.

CONCLUSIONS

Existing systemic data in the EHR has some predictive value in identifying POAG patients at risk of progression to surgical intervention, even in the absence of eye-specific data. Blood pressure-related metrics and certain medication classes emerged as predictors of glaucoma progression. This approach provides an opportunity for future development of automated risk prediction within the EHR based on systemic data to assist with clinical decision-making.