Comparing Prescribing and Dispensing Data of the PCORnet Common Data Model Within PCORnet Antibiotics and Childhood Growth Study.
ABSTRACT: Researchers often use prescribing data from electronic health records (EHR) or dispensing data from medication or medical claims to determine medication utilization. However, neither source has complete information on medication use. We compared antibiotic prescribing and dispensing records for 200,395 patients in the National Patient-Centered Clinical Research Network (PCORnet) Antibiotics and Childhood Growth Study. We stratified analyses by delivery system type [closed integrated (cIDS) and non-cIDS]; 90.5 percent and 39.4 percent of prescribing records had matching dispensing records, and 92.7 percent and 64.0 percent of dispensing records had matching prescribing records at cIDS and non-cIDS, respectively. Most of the dispensings without a matching prescription did not have same-day encounters in the EHR, suggesting they were medications given outside the institution providing data, such as those from urgent care or retail clinics. The sensitivity of prescriptions in the EHR, using dispensings as a gold standard, was 99.1 percent and 89.9 percent for cIDS and non-cIDS, respectively. Only 0.7 percent and 6.1 percent of patients at cIDS and non-cIDS, respectively, were classified as false-negative, i.e. entirely unexposed to antibiotics when they in fact had dispensings. These patients were more likely to have a complex chronic condition or asthma. Overall, prescription records worked well to identify exposure to antibiotics. EHR data, such as the data available in PCORnet, is a unique and vital resource for clinical research. Closing data gaps by understanding why prescriptions may not be captured can improve this type of data, making it more robust for observational research.
Project description:<h4>Objective</h4>Case management programs for high-need high-cost patients are spreading rapidly among health systems. PCORNet has substantial potential to support learning health systems in rapidly evaluating these programs, but access to complete patient data on health care utilization is limited as PCORNet is based on electronic health records not health insurance claims data. Because matching cases to comparison patients on baseline utilization is often a critical component of high-quality observational comparative effectiveness research for high-need high-cost patients, limited access to claims may negatively affect the quality of the matching process. We sought to determine whether the evaluation of programs for high-need high-cost patients required claims data to match cases to comparison patients.<h4>Materials and methods</h4>A retrospective cohort study design with multiple measures of before-and-after health care utilization for 1935 case management patients and 3833 matched comparison patients aged 18 years and older from 2011 to 2015. EHR and claims data were extracted from 3 health systems participating in PCORNet.<h4>Results</h4>Without matching on claims-based health care utilization, the case management programs at 2 of 3 health systems were associated with fewer hospital admissions and emergency visits over the subsequent 12 months. With matching on claims-based health care utilization, case management was no longer associated with admissions and emergency visits at those 2 programs.<h4>Discussion</h4>The results of a PCORNet-facilitated evaluation of 3 programs for high-need high-cost patients differed substantially depending on whether claims data were available for matching cases to comparison patients.<h4>Conclusions</h4>Partnering with learning health systems to rapidly evaluate programs for high-need high-cost patients will require that PCORNet facilitates comprehensive and timely access to both electronic health records and health insurance claims data.
Project description:<b>Background: </b>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.<br><br><b>Methods: </b>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.<br><br><b>Results: </b>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).<br><br><b>Conclusions: </b>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.
Project description:Objective:Medication adherence is an important aspect of chronic disease management. Electronic health record (EHR) data are often not linked to dispensing data, limiting clinicians' understanding of which of their patients fill their medications, and how to tailor care appropriately. We aimed to develop an algorithm to link EHR prescribing to claims-based dispensing data and use the results to quantify how often patients with diabetes filled prescribed chronic disease medications. Materials and Methods:We developed an algorithm linking EHR prescribing data (RxNorm terminology) to claims-based dispensing data (NDC terminology), within sample of adult (19-64) community health center (CHC) patients with diabetes from a network of CHCs across 12 states. We demonstrate an application of the method by calculating dispense rates for a set of commonly prescribed diabetes and cardio-protective medications. To further inform clinical care, we computed adjusted odds ratios of dispense by patient-, encounter-, and clinic-level characteristics. Results:Seventy-six percent of cardio-protective medication prescriptions and 74% of diabetes medications were linked to a dispensing record. Age, income, ethnicity, insurance, assigned primary care provider, comorbidity, time on EHR, and clinic size were significantly associated with odds of dispensing. Discussion:EHR prescriptions and pharmacy dispense data can be linked at the record level across different terminologies. Dispensing rates in this low-income population with diabetes were similar to other populations. Conclusion:Record linkage resulted in the finding that CHC patients with diabetes largely had their chronic disease medications dispensed. Understanding factors associated with dispensing rates highlight barriers and opportunities for optimal disease management.
Project description:<h4>Importance</h4>Benzodiazepines have been a common target for policy interventions to curtail inappropriate use, with mixed results. To reduce alprazolam misuse, in February 2017, Australia delisted the 2-mg tablet strength from public subsidy, eliminated refills, and reduced the pack size from 50 tablets to 10 tablets.<h4>Objective</h4>To describe changes in alprazolam dispensing, prescribing, and poisonings associated with the implementation of a new policy to reduce inappropriate prescription of alprazolam in Australia.<h4>Design, setting, and participants</h4>This interrupted time series analysis and cross-sectional study included data from a 10% sample of Australian people who received publicly subsidized dispensing claims and prescribing approvals for alprazolam from January 1, 2015, to December 31, 2018, and all calls to a poison information service involving alprazolam from February 1, 2015, to October 31, 2018. Autoregressive error models were used to quantify changes over time and compare patterns of use before and after the intervention. Data analyses were conducted from November 2018 to May 2019.<h4>Exposure</h4>Implementation of the policy change on February 1, 2017.<h4>Main outcomes and measures</h4>Monthly trends in alprazolam prescribing approvals and dispensings, quarterly trends in telephone calls involving alprazolam to a poison information service, and patterns of prescribing and dispensing before and after the intervention.<h4>Results</h4>From 2015 to 2018, there were 71?481 alprazolam dispensings to 6772 people. After the intervention, overall dispensing decreased by 51.2% (95% CI, 50.5%-51.9%) and prescribing approvals increased by 17.5% (95% CI, 13.0%-22.2%). Overall, the proportion of dispensing of packs of 51 to 100 tablets increased from 5776 of 24?282 dispensings (23.8%) to 4888 of 10?676 dispensings (45.8%) (risk difference [RD], 22.0% [95% CI, 19.4%-24.6%]) and dispensing of packs of more than 100 tablets increased from 1029 of 24?282 dispensings (4.2%) to 1774 of 10?676 dispensings (16.6%) (RD, 12.4% [95% CI, 10.6%-14.2%]). Among people receiving their first alprazolam prescription, initiation with packs of 10 tablets or fewer increased from 16 of 1127 people (1.4%) before the intervention to 139 of 589 people (23.6%) after the intervention (RD, 22.2% [95% CI, 18.7%-25.7%]). Alprazolam treatment initiation with packs of more than 50 tablets also increased from 63 of 1127 people (5.6%) before the intervention to 144 of 589 people (24.4%) after the intervention (RD, 18.9% [95% CI, 15.1%-22.6%]). During 1 year before the intervention, patients received a median (interquartile range [IQR]) total of 250 (50-600) tablets and median (IQR) total combined tablet strength of 188 (50-550) mg. During 1 year after the intervention, people were dispensed less alprazolam, with a median (IQR) total of 200 (50-500) tablets and median (IQR) total combined tablet strength of 120 (30-360) mg. There was little change in poisoning calls involving alprazolam.<h4>Conclusions and relevance</h4>This study found that after the policy intervention, subsidized alprazolam use decreased, but the increase in prescribing approvals placed additional burden on prescribers. Even after the intervention, most people who were dispensed alprazolam were still receiving treatment contrary to best-practice recommendations. Furthermore, the poison information center data suggested that people were still being dispensed the 2-mg tablet strength, presumably as nonsubsidized (ie, private) prescriptions.
Project description:PURPOSE:PCORnet, the National Patient-Centered Clinical Research Network, represents an innovative system for the conduct of observational and pragmatic studies. We describe the identification and validation of a retrospective cohort of patients with type 2 diabetes (T2DM) from four PCORnet sites. METHODS:We adapted existing computable phenotypes (CP) for the identification of patients with T2DM and evaluated their performance across four PCORnet sites (2012-2016). Patients entered the cohort on the earliest date they met one of three CP categories: (CP1) coded T2DM diagnosis (ICD-9/ICD-10) and an antidiabetic prescription, (CP2) diagnosis and glycosylated hemoglobin (HbA1c) ?6.5%, or (CP3) an antidiabetic prescription and HbA1c ?6.5%. We required evidence of health care utilization in each of the 2 prior years for each patient, as we also developed an incident T2DM CP to identify the subset of patients without documentation of T2DM in the 365 days before t0 . Among a systematic sample of patients, we calculated the positive predictive value (PPV) for the T2DM CP and incident-T2DM CP using electronic health record (EHR) review as reference. RESULTS:The CP identified 50 657 patients with T2DM. The PPV of patients randomly selected for validation was 96.2% (n = 1572; CI:95.1-97.0) and was consistently high across sites. The PPV for the incident-T2DM CP was 5.8% (CI:4.5-7.5). CONCLUSIONS:The T2DM CP accurately and efficiently identified patients with T2DM across multiple sites that participate in PCORnet, although the incident T2DM CP requires further study. PCORnet is a valuable data source for future epidemiological and comparative effectiveness research among patients with T2DM.
Project description:OBJECTIVE:To describe PCORnet, a clinical research network developed for patient-centered outcomes research on a national scale. STUDY DESIGN AND SETTING:Descriptive study of the current state and future directions for PCORnet. We conducted cross-sectional analyses of the health systems and patient populations of the 9 Clinical Research Networks and 2 Health Plan Research Networks that are part of PCORnet. RESULTS:Within the Clinical Research Networks electronic health data are currently collected from 337 hospitals, 169,695 physicians, 3,564 primary care practices, 338 emergency departments, and 1,024 community clinics. Patients can be recruited for prospective studies from any of these clinical sites. The Clinical Research Networks have accumulated data from 80 million patients with at least 1 visit from 2009-2018. The PCORnet Health Plan Research Network population of individuals with a valid enrollment segment from 2009-2019 exceeds 60 million individuals, who on average have 2.63 years of follow-up. CONCLUSION:PCORnet's infrastructure comprises clinical data from a diverse cohort of patients and has the capacity to rapidly access these patient populations for pragmatic clinical trials, epidemiological research, and patient-centered research on rare diseases.
Project description:BACKGROUND:Proton pump inhibitor (PPI) use is widespread. There have been increasing concerns about overuse of high-dose PPIs for durations longer than clinically necessary. OBJECTIVE:To evaluate the impact of national education initiatives on reducing PPI use in Australia. DESIGN:Population-based, controlled interrupted time series analysis of PPI dispensing claims data for Australian adults from July 2012 to June 2018; we used statin dispensing as a control. INTERVENTIONS:A year-long educational initiative led by NPS MedicineWise (previously the National Prescribing Service) from April 2015. Simultaneously, Choosing Wisely released recommendations in April 2015 and May 2016. Both promoted review of prolonged PPI use and encouraged stepping down or ceasing treatment, where appropriate. MEASUREMENTS:We examined monthly changes in PPI (and statin) dispensing (stratified by high, standard and low tablet strength), rates of switching from higher to lower strength PPIs and rates of PPI (and statin) discontinuation. RESULTS:We observed 12 040 021 PPI dispensings to 579?594 people. We observed a sustained -1.7% (95% CI: -2.7 to -0.7%) decline in monthly dispensing of standard strength PPIs following the initiatives until the end of the study period. There were no significant changes in high or low strength PPI (or statin) dispensings, switching to lower strength PPIs, or PPI (and statin) treatment discontinuation. CONCLUSION:Our findings suggest that these educational initiatives alone were insufficient in curbing overuse of PPIs on a national level. Concerted efforts with policy levers such as imposing tighter restrictions on subsidised use of PPIs may be more effective. Noting low strength esomeprazole is not publicly subsidised in Australia, availability of these preparations may also facilitate more appropriate practice.
Project description:The aims of this study were to evaluate the effects on opioid medication prescribing, patient opioid safety education, and prescribing of naloxone following implementation of a Safer Opioid Prescribing Protocol (SOPP) as part of the electronic health record (EHR) system at a Level I trauma center. This was a prospective observational study of the EHR of trauma patients pre- (n = 191) and post-(n = 316) SOPP implementation between 2014 and 2016. At a comparison Level I trauma site not implementing SOPP, EHRs for the same time period were assessed for any historical trends in opioid and naloxone prescribing. After SOPP implementation, the implementation site increased the use of nonnarcotic pain medication, decreased dispensing high opioid dose (?100 MME [milligram morphine equivalent]), significantly increased the delivery of opioid safety education to patients, and initiated prescribing naloxone. These changes were not found in the comparison site. Opioid prescribing for acute pain can be effectively reduced in a busy trauma setting with a guideline intervention incorporated into an EHR. Guidelines can increase the use of nonnarcotic medications for the treatment of acute pain and increase naloxone coprescription for patients with a higher risk of overdose.
Project description:Introduction:Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data. Methods:PCORnet's Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals. Results:The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners. Discussion:The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.