Racial and ethnic associations with interstitial lung disease and healthcare utilization in patients with systemic sclerosis.

OBJECTIVE: Racial and ethnic differences in presentation and outcomes have been reported in systemic sclerosis (SSc) and SSc-interstitial lung disease (ILD). However, prior studies have limited diversity. We aim to evaluate if there are racial/ethnic differences associated with ILD, time intervals between SSc and ILD and with emergency department (ED) visit or hospitalization rates. METHODS: Clinical and sociodemographic variables were extracted for 756 patients with SSc from longitudinal health records in an integrated health-system. Logistic regression models analyzed the association of covariates with ILD and age at SSc-ILD. Healthcare outcomes were analyzed with complementary log-log regression models. RESULTS: Overall, 33.7% of patients in the cohort had an ILD code, with increased odds for Asian (odds ratio [OR], 2.60; 95% confidence interval [CI], 1.29-5.28; p=0.008) compared with White patients. The predicted age in years of SSc-ILD was younger for Hispanic (estimate, -6.5; 95% CI, -13--0.21; p = 0.04) and Black/African American patients (-10; 95% CI -16--4.9; p < 0.001) compared with White patients. Black/African American patients were more likely to have an ILD code before an SSc code (59% compared with 20.6% of White patients), and the shortest interval from SSc to ILD (3 months). Black/African American (HR, 2.59; 95% CI 1.47-4.49; p = 0.001) and Hispanic patients (HR 2.29; 95% CI 1.37- 3.82; p = 0.002) had higher rates of an ED visit. CONCLUSION: We found that odds of SSc-ILD differed by racial/ethnic group, minoritized patients had earlier age of presentation, and greater rates of an ED visit.

A machine learning technology for addressing medication-related risk in older, multimorbid patients.

OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitalizations, and to assess the system's ability to provide accurate medication recommendations for these patients. STUDY DESIGN: Retrospective cohort study. METHODS: The system uses medications, demographics, diagnoses, laboratory results, health care utilization patterns, and costs to stratify patients' risk of ED visits and hospitalizations. Patients were assigned 1 of 22 risk levels based on their system-generated risk percentile of either ED visits or hospitalizations. Logistic regression models were used to estimate the odds of ED visits and hospitalizations associated with each successive risk level compared with the 45th to 50th percentiles. After stratification, 100 high-risk (95th-100th percentiles) and 100 medium-risk (45th-55th percentiles) patients were randomly selected for generation of medication recommendations. Two clinical pharmacists reviewed the system-generated medication recommendations for these patients. RESULTS: Logistic regression models predicting 3-month utilization showed that compared with the 45th to 50th percentiles, patients in the top 1% risk percentile had ORs of 7.9 and 17.3 for ED visits and hospitalizations, respectively. The first 5 high-priority medications on each patient's medication list were associated with a mean (SD) of 6.65 (4.09) warnings. Of 1290 warnings reviewed, 1151 (89.2%) were assessed as correct. CONCLUSIONS: The FeelBetter system effectively stratifies older patients with multimorbidity at risk of ED use and hospitalizations. Medication recommendations provided by the system are largely accurate and can potentially be beneficial for patient care.

Frequency and preventability of adverse drug events in the outpatient setting.

BACKGROUND: Limited data exist regarding adverse drug events (ADEs) in the outpatient setting. The objective of this study was to determine the incidence, severity, and preventability of ADEs in the outpatient setting and identify potential prevention strategies. METHODS: We conducted an analysis of ADEs identified in a retrospective electronic health records review of outpatient encounters in 2018 at 13 outpatient sites in Massachusetts that included 13 416 outpatient encounters in 3323 patients. Triggers were identified in the medical record including medications, consultations, laboratory results, and others. If a trigger was detected, a further in-depth review was conducted by nurses and adjudicated by physicians to examine the relevant information in the medical record. Patients were included in the study if they were at least 18 years of age with at least one outpatient encounter with a physician, nurse practitioner or physician's assistant in that calendar year. Patients were excluded from the study if the outpatient encounter occurred in outpatient surgery, psychiatry, rehabilitation, and paediatrics. RESULTS: In all, 5% of patients experienced an ADE over the 1-year period. We identified 198 ADEs among 170 patients, who had a mean age of 60. Most patients experienced one ADE (87%), 10% experienced two ADEs and 3% experienced three or more ADEs. The most frequent drug classes resulting in ADEs were cardiovascular (25%), central nervous system (14%), and anti-infective agents (14%). Severity was ranked as significant in 85%, 14% were serious, 1% were life-threatening, and there were no fatal ADEs. Of the ADEs, 22% were classified as preventable and 78% were not preventable. We identified 246 potential prevention strategies, and 23% of ADEs had more than one prevention strategy possibility. CONCLUSIONS: Despite efforts to prioritise patient safety, medication-related harms are still frequent. These results underscore the need for further patient safety improvement in the outpatient setting.

A Calculated Risk: Evaluation of QTc Drug-Drug Interaction (DDI) Clinical Decision Support (CDS) Alerts and Performance of the Tisdale Risk Score Calculator.

INTRODUCTION: A risk factor for a potentially fatal ventricular arrhythmia Torsade de Pointes is a prolongation in the heart rate-corrected QT interval (QTc) ≥ 500 milliseconds (ms) or an increase of ≥ 60 ms from a patient's baseline value, which can cause sudden cardiac death. The Tisdale risk score calculator uses clinical variables to predict which hospitalized patients are at the highest risk for QTc prolongation. OBJECTIVE: To determine the rate of overridden QTc drug-drug interaction (DDI)-related clinical decision support (CDS) alerts per patient admission and the prevalence by Tisdale risk score category of these overridden alerts. Secondary outcome was to determine the rate of drug-induced QTc prolongation (diQTP) associated with overrides. METHODS: Our organization's enterprise data warehouse was used to retrospectively access QTc DDI alerts presented for patients aged ≥ 18 years who were admitted to Brigham and Women's Hospital during 2022. The QTc DDI CDS alerts were included if shown to a physician, fellow, resident, physician assistant, or nurse practitioner when entering the order in inpatient areas for patients with a length of stay of at least 2 days. Variables collected for the Tisdale calculator included age, sex, whether patient was on a loop diuretic, potassium level, admission QTc value, admitting diagnosis of acute myocardial infarction, sepsis, or heart failure, and number of QTc-prolonging drugs given to the patient. RESULTS: A total of 2649 patients with 3033 patient admissions had 18,432 QTc DDI alerts presented that were overridden. An average of 3 unique QTc DDI alerts were presented per patient admission and the alerts were overridden an average of 6 times per patient admission. Overall, 6% of patient admissions were low risk (score ≤ 6), 64% moderate risk (score 7-10), and 30% high risk (score ≥ 11) of QTc prolongation. The most common QTc DDI alerts overridden resulting in an diQTP were quetiapine and propofol (11%) and amiodarone and haloperidol (7%). The diQTP occurred in 883 of patient admissions (29%) and was more frequent in those with higher risk score, with 46% of patient admissions with diQTP in high risk, 23% in moderate risk, and 8% in low risk. CONCLUSION: Use of the Tisdale calculator to assess patient-specific risk of QT prolongation combined with CDS may improve overall alert quality and acceptance rate, which may decrease the diQTP rate.

Straight to the point: evaluation of a Point of Care Information (POCI) resource in answering disease-related questions.

Objective: To evaluate the ability of DynaMedex, an evidence-based drug and disease Point of Care Information (POCI) resource, in answering clinical queries using keyword searches. Methods: Real-world disease-related questions compiled from clinicians at an academic medical center, DynaMedex search query data, and medical board review resources were categorized into five clinical categories (complications & prognosis, diagnosis & clinical presentation, epidemiology, prevention & screening/monitoring, and treatment) and six specialties (cardiology, endocrinology, hematology-oncology, infectious disease, internal medicine, and neurology). A total of 265 disease-related questions were evaluated by pharmacist reviewers based on if an answer was found (yes, no), whether the answer was relevant (yes, no), difficulty in finding the answer (easy, not easy), cited best evidence available (yes, no), clinical practice guidelines included (yes, no), and level of detail provided (detailed, limited details). Results: An answer was found for 259/265 questions (98%). Both reviewers found an answer for 241 questions (91%), neither found the answer for 6 questions (2%), and only one reviewer found an answer for 18 questions (7%). Both reviewers found a relevant answer 97% of the time when an answer was found. Of all relevant answers found, 68% were easy to find, 97% cited best quality of evidence available, 72% included clinical guidelines, and 95% were detailed. Recommendations for areas of resource improvement were identified. Conclusions: The resource enabled reviewers to answer most questions easily with the best quality of evidence available, providing detailed answers and clinical guidelines, with a high level of replication of results across users.

Racial and ethnic associations with interstitial lung disease and healthcare utilization in patients with systemic sclerosis.

Rationale: Racial and ethnic differences in presentation and outcomes have been reported in systemic sclerosis (SSc) and SSc-interstitial lung disease (ILD). However, diverse cohorts and additional modeling can improve understanding of risk features and outcomes, which is important for reducing associated disparities. Objectives: To determine if there are racial/ethnic differences associated with SSc-ILD risk and age; time intervals between SSc and ILD, and with emergency department (ED) visit or hospitalization rates. Methods: A retrospective cohort study using electronic health record data from an integrated health system, over a 5.5 year period was conducted using clinical and sociodemographic variables, models were generated with sequential adjustments for these variables. Logistic regression models were used to examine the association of covariates with ILD and age at SSc-ILD. Healthcare outcomes were analyzed with complementary log-log regression models. Results: The cohort included 756 adults (83.6% female, 80.3% non-Hispanic White) with SSc with a mean age of 59 years. Overall, 33.7% of patients in the cohort had an ILD code, with increased odds for Asian (odds ratio [OR], 2.59; 95% confidence interval [CI], 1.29, 5.18; P =.007) compared to White patients. The age in years of patients with SSc-ILD was younger for Hispanic (mean difference, -6.5; 95% CI, -13, -0.21; P = 0.04) and Black/African American patients (-10; 95% CI -16, -4.9; P <0.001) compared to White patients. Black/African American patients were more likely to have an ILD code before an SSc code (59% compared to 20.6% of White patients), and had the shortest interval from SSc to ILD (3 months). Black/African American (HR, 2.59; 95% CI 1.47, 4.49; P =0.001) and Hispanic patients (HR 2.29; 95% CI 1.37, 3.82; P =0.002) had higher rates of an ED visit. Conclusion: In this study, SSc-ILD presentation and outcomes differed by racial/ethnic group (increased odds of SSc-ILD, younger age at SSc-ILD, and preceding diagnosis with respect to SSc, rates of ED visit), some of which was attenuated with adjustment for clinical and sociodemographic characteristics. Differing presentation may be driven by social drivers of health (SDOH), autoantibody profiles, or other key unmeasured factors contributing to susceptibility and severity.

How Safe are Outpatient Electronic Health Records? An Evaluation of Medication-Related Decision Support using the Ambulatory Electronic Health Record Evaluation Tool.

BACKGROUND: The purpose of the Ambulatory Electronic Health Record (EHR) Evaluation Tool is to provide outpatient clinics with an assessment that they can use to measure the ability of the EHR system to detect and prevent common prescriber errors. The tool consists of a medication safety test and a medication reconciliation module. OBJECTIVES: The goal of this study was to perform a broad evaluation of outpatient medication-related decision support using the Ambulatory EHR Evaluation Tool. METHODS: We performed a cross-sectional study with 10 outpatient clinics using the Ambulatory EHR Evaluation Tool. For the medication safety test, clinics were provided test patients and associated medication test orders to enter in their EHR, where they recorded any advice or information they received. Once finished, clinics received an overall percentage score of unsafe orders detected and individual order category scores. For the medication reconciliation module, clinics were asked to electronically reconcile two medication lists, where modifications were made by adding and removing medications and changing the dosage of select medications. RESULTS: For the medication safety test, the mean overall score was 57%, with the highest score being 70%, and the lowest score being 40%. Clinics performed well in the drug allergy (100%), drug dose daily (85%), and inappropriate medication combinations (74%) order categories. Order categories with the lowest performance were drug laboratory (10%) and drug monitoring (3%). Most clinics (90%) scored a 0% in at least one order category. For the medication reconciliation module, only one clinic (10%) could reconcile medication lists electronically; however, there was no clinical decision support available that checked for drug interactions. CONCLUSION: We evaluated a sample of ambulatory practices around their medication-related decision support and found that advanced capabilities within these systems have yet to be widely implemented. The tool was practical to use and identified substantial opportunities for improvement in outpatient medication safety.

Correction: Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

[This corrects the article DOI: 10.2196/43960.].

Development and validation of algorithms to build an electronic health record based cohort of patients with systemic sclerosis.

OBJECTIVES: To evaluate methods of identifying patients with systemic sclerosis (SSc) using International Classification of Diseases, Tenth Revision (ICD-10) codes (M34*), electronic health record (EHR) databases and organ involvement keywords, that result in a validated cohort comprised of true cases with high disease burden. METHODS: We retrospectively studied patients in a healthcare system likely to have SSc. Using structured EHR data from January 2016 to June 2021, we identified 955 adult patients with M34* documented 2 or more times during the study period. A random subset of 100 patients was selected to validate the ICD-10 code for its positive predictive value (PPV). The dataset was then divided into a training and validation sets for unstructured text processing (UTP) search algorithms, two of which were created using keywords for Raynaud's syndrome, and esophageal involvement/symptoms. RESULTS: Among 955 patients, the average age was 60. Most patients (84%) were female; 75% of patients were White, and 5.2% were Black. There were approximately 175 patients per year with the code newly documented, overall 24% had an ICD-10 code for esophageal disease, and 13.4% for pulmonary hypertension. The baseline PPV was 78%, which improved to 84% with UTP, identifying 788 patients likely to have SSc. After the ICD-10 code was placed, 63% of patients had a rheumatology office visit. Patients identified by the UTP search algorithm were more likely to have increased healthcare utilization (ICD-10 codes 4 or more times 84.1% vs 61.7%, p < .001), organ involvement (pulmonary hypertension 12.7% vs 6% p = .011) and medication use (mycophenolate use 28.7% vs 11.4%, p < .001) than those identified by the ICD codes alone. CONCLUSION: EHRs can be used to identify patients with SSc. Using unstructured text processing keyword searches for SSc clinical manifestations improved the PPV of ICD-10 codes alone and identified a group of patients most likely to have SSc and increased healthcare needs.

Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

BACKGROUND: Evidence-based point-of-care information (POCI) tools can facilitate patient safety and care by helping clinicians to answer disease state and drug information questions in less time and with less effort. However, these tools may also be visually challenging to navigate or lack the comprehensiveness needed to sufficiently address a medical issue. OBJECTIVE: This study aimed to collect clinicians' feedback and directly observe their use of the combined POCI tool DynaMed and Micromedex with Watson, now known as DynaMedex. EBSCO partnered with IBM Watson Health, now known as Merative, to develop the combined tool as a resource for clinicians. We aimed to identify areas for refinement based on participant feedback and examine participant perceptions to inform further development. METHODS: Participants (N=43) within varying clinical roles and specialties were recruited from Brigham and Women's Hospital and Massachusetts General Hospital in Boston, Massachusetts, United States, between August 10, 2021, and December 16, 2021, to take part in usability sessions aimed at evaluating the efficiency and effectiveness of, as well as satisfaction with, the DynaMed and Micromedex with Watson tool. Usability testing methods, including think aloud and observations of user behavior, were used to identify challenges regarding the combined tool. Data collection included measurements of time on task; task ease; satisfaction with the answer; posttest feedback on likes, dislikes, and perceived reliability of the tool; and interest in recommending the tool to a colleague. RESULTS: On a 7-point Likert scale, pharmacists rated ease (mean 5.98, SD 1.38) and satisfaction (mean 6.31, SD 1.34) with the combined POCI tool higher than the physicians, nurse practitioner, and physician's assistants (ease: mean 5.57, SD 1.64, and satisfaction: mean 5.82, SD 1.60). Pharmacists spent longer (mean 2 minutes, 26 seconds, SD 1 minute, 41 seconds) on average finding an answer to their question than the physicians, nurse practitioner, and physician's assistants (mean 1 minute, 40 seconds, SD 1 minute, 23 seconds). CONCLUSIONS: Overall, the tool performed well, but this usability evaluation identified multiple opportunities for improvement that would help inexperienced users.

Dynamic reaction picklist for improving allergy reaction documentation: A usability study.

OBJECTIVE: To assess novel dynamic reaction picklists for improving allergy reaction documentation compared to a static reaction picklist. MATERIALS AND METHODS: We developed three web-based user interfaces (UIs) mimicking the Mass General Brigham's EHR allergy module: the first and second UIs (i.e., UI-1D, UI-2D) implemented two dynamic reaction picklists with different ranking algorithms and the third UI (UI-3S) implemented a static reaction picklist like the one used in the current EHR. We recruited 18 clinicians to perform allergy entry for 10 test cases each via UI-1D and UI-3S, and another 18 clinicians via UI-2D and UI-3S. Primary measures were the number of free-text entries and time to complete the allergy entry. Clinicians were also interviewed using 30 questions before and after the data entry. RESULTS AND DISCUSSIONS: Among 36 clinicians, less than half were satisfied with the current EHR reaction picklists, due to their incomprehensiveness, inefficiency, and lack of intuitiveness. The clinicians used significantly fewer free-text entries when using UI-1D or UI-2D compared to UI-3S (p < 0.05). The clinicians used on average 51 s (15 %) less time via UI-1D and 50 s (16 %) less time via UI-2D in completing the allergy entries versus UI-3S, and there was not a statistically significant difference in documentation time for either group between the dynamic and static UIs. Overall, 15-17 (83-94 %) clinicians rated UI-1D and 13-15 (72-83 %) clinicians rated UI-2D as efficient, easy to use, and useful, while less than half rated the same for UI-3S. Most clinicians reported that the dynamic reaction picklists always or often suggested appropriate reactions (n = 30, 83 %) and would decrease the free-text entries (n = 26, 72 %); nearly all preferred the dynamic picklist over the static picklist (n = 32, 89 %). CONCLUSION: We found that dynamic reaction picklists significantly reduced the number of free-text entries and could reduce the time for allergy documentation by 15%. Clinicians preferred the dynamic reaction picklist over the static picklist.

Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing.

Background: Drug challenge tests serve to evaluate whether a patient is allergic to a medication. However, the allergy list in the electronic health record (EHR) is not consistently updated to reflect the results of the challenge, affecting clinicians' prescription decisions and contributing to inaccurate allergy labels, inappropriate drug-allergy alerts, and potentially ineffective, more toxic, and/or costly care. In this study, we used natural language processing (NLP) to automatically detect discrepancies between the EHR allergy list and drug challenge test results and to inform the clinical recommendations provided in a real-time allergy reconciliation module. Methods: This study included patients who received drug challenge tests at the Mass General Brigham (MGB) Healthcare System between June 9, 2015 and January 5, 2022. At MGB, drug challenge tests are performed in allergy/immunology encounters with routine clinical documentation in notes and flowsheets. We developed a rule-based NLP tool to analyze and interpret the challenge test results. We compared these results against EHR allergy lists to detect potential discrepancies in allergy documentation and form a recommendation for reconciliation if a discrepancy was identified. To evaluate the capability of our tool in identifying discrepancies, we calculated the percentage of challenge test results that were not updated and the precision of the NLP algorithm for 200 randomly sampled encounters. Results: Among 200 samples from 5,312 drug challenge tests, 59% challenged penicillin reactivity and 99% were negative. 42.0%, 61.5%, and 76.0% of the results were confirmed by flowsheets, NLP, or both, respectively. The precision of the NLP algorithm was 96.1%. Seven percent of patient allergy lists were not updated based on drug challenge test results. Flowsheets alone were used to identify 2.0% of these discrepancies, and NLP alone detected 5.0% of these discrepancies. Because challenge test results can be recorded in both flowsheets and clinical notes, the combined use of NLP and flowsheets can reliably detect 5.5% of discrepancies. Conclusion: This NLP-based tool may be able to advance global delabeling efforts and the effectiveness of drug allergy assessments. In the real-time EHR environment, it can be used to examine patient allergy lists and identify drug allergy label discrepancies, mitigating patient risks.

Identifying and Reconciling Patients' Allergy Information Within the Electronic Health Record.

Allergy information is often documented in diverse sections of the electronic health record (EHR). Systematically reconciling allergy information across the EHR is critical to improve the accuracy and completeness of patients' allergy lists and ensure patient safety. In this retrospective cohort study, we examined the prevalence of incompleteness, inaccuracy, and redundancy of allergy information for patients with a clinical encounter at any Mass General Brigham facility between January 1, 2018 and December 31, 2018. We identified 4 key places in the EHR containing reconcilable allergy information: 1) allergy modules (including free text comments and duplicate allergen entries), 2) medication laboratory tests results, 3) oral medication allergy challenge tests, and 4) medication orders that have been discontinued due to adverse drug reactions (ADRs). Within our cohort, 718,315 (45.2% of the total 1,588,979) patients had an active allergy entry; of which, 266,275 (37.1%) patient's records indicated a need for reconciliation.

Heterogeneity of Drug Allergies and Reaction Lists in Two U.S. Health Care Systems' Electronic Health Records.

BACKGROUND: Health care institutions have their own "picklist" for clinicians to document adverse drug reactions (ADRs) into the electronic health record (EHR) allergy list. Whether the lack of a nationally standardized picklist impacts clinician data entries is unknown. OBJECTIVES: The objective of this study was to assess the impact of defined reaction picklists on clinical documentation and, therefore, downstream analytics and clinical research using these data at two institutions. METHODS: ADR data were obtained from the EHRs of patients who visited the emergency department or outpatient clinics at Brigham and Women's Hospital (BWH) and University of Colorado Hospital (UCH) from 2013 to 2018. Reported drug class ADR prevalences were calculated. We investigated the reactions on each picklist and compared the top 40 reactions at each institution, as well as the top 10 reactions within each drug class. RESULTS: Of 2,160,116 patients, 640,444 (30%) had 928,973 active drug allergies. The most commonly reported drug class allergens were similar between BWH and UCH. BWH's picklist had 48 reactions, and UCH's had 160 reactions; 29 reactions were shared by both picklists. While the top four reactions overall (rash, GI upset/nausea/vomiting, hives, itching) were identical between sites, reactions by drug class exhibited greater documentation diversity. For example, while the summed prevalence of swelling-related reactions to angiotensin-converting-enzyme inhibitors was comparable across sites, swelling was represented by two terms ("swelling," "angioedema") at BWH but 11 terms at UCH (e.g., "swelling," "edema," by body locality). CONCLUSION: The availability and granularity of reaction picklists impact ADR documentation in the EHR by health care providers; picklists may partially explain variations in reported ADRs across health care systems.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review.

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned.

BACKGROUND: Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed. OBJECTIVE: To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot. METHODS: The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group. The tool was piloted with seven clinics in the United States, each using a different EHR. The tool consists of a medication safety test and a medication reconciliation module. For the medication test, clinics entered test patients and associated test orders into their EHR and recorded any decision support they received. An overall percentage score of unsafe orders detected, and order category scores were provided to clinics. For the medication reconciliation module, clinics demonstrated how their EHR electronically detected discrepancies between two medication lists. RESULTS: For the medication safety test, the clinics correctly alerted on 54.6% of unsafe medication orders. Clinics scored highest in the drug allergy (100%) and drug-drug interaction (89.3%) categories. Lower scoring categories included drug age (39.3%) and therapeutic duplication (39.3%). None of the clinics alerted for the drug laboratory or drug monitoring orders. In the medication reconciliation module, three (42.8%) clinics had an EHR-based medication reconciliation function; however, only one of those clinics could demonstrate it during the pilot. CONCLUSION: Clinics struggled in areas of advanced decision support such as drug age, drug laboratory, and drub monitoring. Most clinics did not have an EHR-based medication reconciliation function and this process was dependent on accessing patients' medication lists. Wider use of this tool could improve outpatient medication safety and can inform vendors about areas of improvement.

Comparison of Medication Alerts from Two Commercial Applications in the USA.

INTRODUCTION: Medication organizations across the USA have adopted electronic health records, and one of the most anticipated benefits of these was improved medication safety, but alert fatigue has been a major issue. OBJECTIVE: We compared the appropriateness of medication-related clinical decision support alerts triggered by two commercial applications: EPIC and Seegnal's platform. METHODS: This was a retrospective comparison of two commercial applications. We provided Seegnal with deidentified inpatient, outpatient, and inpatient genetic electronic medical record (EMR)-extracted datasets for 657, 2731, and 413 patients, respectively. Seegnal then provided the alerts that would have triggered, which we compared with those triggered by EPIC in clinical care. A random sample of the alerts triggered were reviewed for appropriateness, and the positive predictive value (PPV) and negative predictive value (NPV) were calculated. We also reviewed all the inpatient and outpatient charts for patients within our cohort who were receiving ten or more concomitant medications with alerts we found to be appropriate to assess whether any adverse events had occurred and whether Seegnal's platform could have prevented them. RESULTS: Results from EPIC and the Seegnal platform were compared based on alert load, PPV, NPV, and potential adverse events. Overall, compared with EPIC, the Seegnal platform triggered fewer alerts in the inpatient (1697 vs. 27,540), outpatient (2341 vs. 35,134), and inpatient genetic (1493 vs. 20,975) cohorts. The Seegnal platform had higher specificity in the inpatient (99 vs. 0.3%; p < 0.0001), outpatient (99 vs. 0.3%; p < 0.0001), and inpatient genetic (97.9 vs. 1.2%; p < 0.0001) groups and higher sensitivity in the inpatient (100 vs. 68.8%; p < 0.0001) and outpatient (88.6 vs.78.3%; p < 0.0001) groups but not in the inpatient genetic cohort (81 vs. 78.5%; p = 0.11). We identified 16 adverse events that occurred in the inpatient setting, 11 (69%) of which potentially could have been prevented with the Seegnal platform. CONCLUSIONS: Overall, the Seegnal platform triggered 94% fewer alerts than EPIC in the inpatient setting and 93% fewer in the outpatient setting, with much higher sensitivity and specificity. This application could substantially reduce alert fatigue and improve medication safety at the same time.

Clinical Utility of Pharmacogenomic Data Collected by a Health-System Biobank to Predict and Prevent Adverse Drug Events.

INTRODUCTION: Medication-related harm represents a significant issue for patient safety and quality of care. One strategy to avoid preventable adverse drug events is to utilize patient-specific factors such as pharmacogenomics (PGx) to individualize therapy. OBJECTIVE: We measured the number of patients enrolled in a health-system biobank with actionable PGx results who received relevant medications and assessed the incidence of adverse drug events (ADEs) that might have been prevented had the PGx results been used to inform prescribing. METHODS: Patients with actionable PGx results in the following four genes with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines were identified: HLA-A*31:01, HLA-B*15:02, TPMT, and VKORC1. The patients who received interacting medications (carbamazepine, oxcarbazepine, thiopurines, or warfarin) were identified, and electronic health records were reviewed to determine the incidence of potentially preventable ADEs. RESULTS: Of 36,424 patients with PGx results, 2327 (6.4%) were HLA-A*31:01 positive; 3543 (9.7%) were HLA-B*15:02 positive; 2893 (7.9%) were TPMT intermediate metabolizers; and 4249 (11.7%) were homozygous for the VKORC1 c.1639 G>A variant. Among patients positive for one of the HLA variants who received carbamazepine or oxcarbazepine (n = 92), four (4.3%) experienced a rash that warranted drug discontinuation. Among the TPMT intermediate metabolizers who received a thiopurine (n = 56), 11 (19.6%) experienced severe myelosuppression that warranted drug discontinuation. Among patients homozygous for the VKORC1 c.1639 G>A variant who received warfarin (n = 379), 85 (22.4%) experienced active bleeding and/or international normalized ratio (INR) > 5 that warranted drug discontinuation or dose reduction. CONCLUSION: Patients with actionable PGx results from a health-system biobank who received relevant medications experienced predictable ADEs. These ADEs may have been prevented if the patients' PGx results were available in the electronic health record with clinical decision support prior to prescribing.

Renal medication-related clinical decision support (CDS) alerts and overrides in the inpatient setting following implementation of a commercial electronic health record: implications for designing more effective alerts.

OBJECTIVE: To assess the appropriateness of medication-related clinical decision support (CDS) alerts associated with renal insufficiency and the potential/actual harm from overriding the alerts. MATERIALS AND METHODS: Override rate frequency was recorded for all inpatients who had a renal CDS alert trigger between 05/2017 and 04/2018. Two random samples of 300 for each of 2 types of medication-related CDS alerts associated with renal insufficiency-"dose change" and "avoid medication"-were evaluated by 2 independent reviewers using predetermined criteria for appropriateness of alert trigger, appropriateness of override, and patient harm. RESULTS: We identified 37 100 "dose change" and 5095 "avoid medication" alerts in the population evaluated, and 100% of each were overridden. Dose change triggers were classified as 12.5% appropriate and overrides of these alerts classified as 90.5% appropriate. Avoid medication triggers were classified as 29.6% appropriate and overrides 76.5% appropriate. We identified 5 adverse drug events, and, of these, 4 of the 5 were due to inappropriately overridden alerts. CONCLUSION: Alerts were nearly always presented inappropriately and were all overridden during the 1-year period studied. Alert fatigue resulting from receiving too many poor-quality alerts may result in failure to recognize errors that could lead to patient harm. Although medication-related CDS alerts associated with renal insufficiency had previously been found to be the most clinically beneficial alerts in a legacy system, in this system they were ineffective. These findings underscore the need for improvements in alert design, implementation, and monitoring of alert performance to make alerts more patient-specific and clinically appropriate.

The Burden of Opioid-Related Adverse Drug Events on Hospitalized Previously Opioid-Free Surgical Patients.

OBJECTIVE: Opioid analgesics are a mainstay for acute pain management, but postoperative opioid administration has risks. We examined the prevalence, risk factors, and consequences of opioid-related adverse drug events (ORADEs) in a previously opioid-free surgical population. METHODS: A retrospective, observational, cohort study using administrative, billing, clinical, and medication administration data from two hospitals. Data were collected for all adult patients who were opioid-free at admission, underwent surgery between October 1, 2015, and September 30, 2016, and received postoperative opioids. Potential ORADEs were determined based on inpatient billing codes or postoperative administration of naloxone. We determined independent predictors of ORADE development using multivariable logistic regression. We measured adjusted inpatient mortality, hospital costs, length of hospital stay, discharge destination, and readmission within 30 days for patients with and without ORADEs. RESULTS: Among 13,389 hospitalizations where opioid-free patients had a single qualifying surgery, 12,218 (91%) received postoperative opioids and comprised the study cohort. Of these, we identified 1111 (9.1%) with a potential ORADE. Independent predictors of ORADEs included older age, several markers of disease severity, longer surgeries, and concurrent benzodiazepine use. Opioid-related adverse drug events were strongly associated with the route and duration of opioids administered postoperatively: 18% increased odds per day on intravenous opioids. In analyses adjusted for several covariates, presence of an ORADE was associated with 32% higher costs of hospitalization, 45% longer postoperative length of stay, 36% lower odds of discharge home, and 2.2 times the odds of death. CONCLUSIONS: We demonstrate a high rate and severe consequences of potential ORADEs in previously opioid-free patients receiving postoperative opioids. Knowledge of risk factors and predictors of ORADEs can help develop targeted interventions to minimize the development of these potentially dangerous and costly events.