Antithrombotic Therapy in Patients With Atrial Fibrillation and Coronary Artery Disease Undergoing Percutaneous Coronary Intervention.

AIM: The objective of this article is to review the contemporary literature on the use of antithrombotic therapy in patients with atrial fibrillation (AF) and coronary artery disease after undergoing percutaneous coronary intervention (PCI). Special consideration was given to the type and duration of therapy, treatment strategies for the elderly (≥65 years of age), and strategies to reduce bleeding. METHODS: Relevant studies were searched through MEDLINE/PubMed, Web of Science, Cochrane Library, ClinicalTrials.gov, and Google Scholar. Of the 236 publications retrieved, 76 were considered relevant including 35 randomized controlled trials, 17 meta-analyses, 16 observational studies, and 8 published major guidelines. RESULTS: Most trials, meta-analyses, and guidelines support 1 month of triple therapy (TT) with an oral anticoagulant (OAC), dual antiplatelet agents (DAPT) with aspirin (ASA)/clopidogrel, and, afterward, dual therapy (DT) with OAC and single antiplatelet agent for an additional 11 months, or alternatively DT alone for 12 months after PCI. Individual consideration is given to the risk and impact of stent thrombosis (ST), thromboembolism, and bleeding. Several trials and meta-analyses have also suggested that shorter DAPT duration (≤6 months) may be safer than longer therapy (≥6 months) when weighing the risk of bleeding with ischemic outcomes, especially with newer generation drug-eluting stents. The selective use of proton-pump inhibitors in patients prone to gastrointestinal bleeding who are subjected to prolonged exposure with TT or DT may be beneficial. In the elderly, the risk of bleeding from TT, compared with DT, outweighs the benefit of reducing ischemic events. CONCLUSIONS: In conclusion, tailoring therapy to the individual patient is recommended considering the ischemic and bleeding risk as well as the risk of thromboembolism. For most patients with AF, 1 month of TT and subsequently DT for additional 11 months are recommended.

Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists.

IMPORTANCE: Accurate clinical documentation is critical to health care quality and safety. Dictation services supported by speech recognition (SR) technology and professional medical transcriptionists are widely used by US clinicians. However, the quality of SR-assisted documentation has not been thoroughly studied. OBJECTIVE: To identify and analyze errors at each stage of the SR-assisted dictation process. DESIGN SETTING AND PARTICIPANTS: This cross-sectional study collected a stratified random sample of 217 notes (83 office notes, 75 discharge summaries, and 59 operative notes) dictated by 144 physicians between January 1 and December 31, 2016, at 2 health care organizations using Dragon Medical 360 | eScription (Nuance). Errors were annotated in the SR engine-generated document (SR), the medical transcriptionist-edited document (MT), and the physician's signed note (SN). Each document was compared with a criterion standard created from the original audio recordings and medical record review. MAIN OUTCOMES AND MEASURES: Error rate; mean errors per document; error frequency by general type (eg, deletion), semantic type (eg, medication), and clinical significance; and variations by physician characteristics, note type, and institution. RESULTS: Among the 217 notes, there were 144 unique dictating physicians: 44 female (30.6%) and 10 unknown sex (6.9%). Mean (SD) physician age was 52 (12.5) years (median [range] age, 54 [28-80] years). Among 121 physicians for whom specialty information was available (84.0%), 35 specialties were represented, including 45 surgeons (37.2%), 30 internists (24.8%), and 46 others (38.0%). The error rate in SR notes was 7.4% (ie, 7.4 errors per 100 words). It decreased to 0.4% after transcriptionist review and 0.3% in SNs. Overall, 96.3% of SR notes, 58.1% of MT notes, and 42.4% of SNs contained errors. Deletions were most common (34.7%), then insertions (27.0%). Among errors at the SR, MT, and SN stages, 15.8%, 26.9%, and 25.9%, respectively, involved clinical information, and 5.7%, 8.9%, and 6.4%, respectively, were clinically significant. Discharge summaries had higher mean SR error rates than other types (8.9% vs 6.6%; difference, 2.3%; 95% CI, 1.0%-3.6%; P < .001). Surgeons' SR notes had lower mean error rates than other physicians' (6.0% vs 8.1%; difference, 2.2%; 95% CI, 0.8%-3.5%; P = .002). One institution had a higher mean SR error rate (7.6% vs 6.6%; difference, 1.0%; 95% CI, -0.2% to 2.8%; P = .10) but lower mean MT and SN error rates (0.3% vs 0.7%; difference, -0.3%; 95% CI, -0.63% to -0.04%; P = .03 and 0.2% vs 0.6%; difference, -0.4%; 95% CI, -0.7% to -0.2%; P = .003). CONCLUSIONS AND RELEVANCE: Seven in 100 words in SR-generated documents contain errors; many errors involve clinical information. That most errors are corrected before notes are signed demonstrates the importance of manual review, quality assurance, and auditing.

A value set for documenting adverse reactions in electronic health records.

Objective: To develop a comprehensive value set for documenting and encoding adverse reactions in the allergy module of an electronic health record. Materials and Methods: We analyzed 2 471 004 adverse reactions stored in Partners Healthcare's Enterprise-wide Allergy Repository (PEAR) of 2.7 million patients. Using the Medical Text Extraction, Reasoning, and Mapping System, we processed both structured and free-text reaction entries and mapped them to Systematized Nomenclature of Medicine - Clinical Terms. We calculated the frequencies of reaction concepts, including rare, severe, and hypersensitivity reactions. We compared PEAR concepts to a Federal Health Information Modeling and Standards value set and University of Nebraska Medical Center data, and then created an integrated value set. Results: We identified 787 reaction concepts in PEAR. Frequently reported reactions included: rash (14.0%), hives (8.2%), gastrointestinal irritation (5.5%), itching (3.2%), and anaphylaxis (2.5%). We identified an additional 320 concepts from Federal Health Information Modeling and Standards and the University of Nebraska Medical Center to resolve gaps due to missing and partial matches when comparing these external resources to PEAR. This yielded 1106 concepts in our final integrated value set. The presence of rare, severe, and hypersensitivity reactions was limited in both external datasets. Hypersensitivity reactions represented roughly 20% of the reactions within our data. Discussion: We developed a value set for encoding adverse reactions using a large dataset from one health system, enriched by reactions from 2 large external resources. This integrated value set includes clinically important severe and hypersensitivity reactions. Conclusion: This work contributes a value set, harmonized with existing data, to improve the consistency and accuracy of reaction documentation in electronic health records, providing the necessary building blocks for more intelligent clinical decision support for allergies and adverse reactions.

Prevalence of food allergies and intolerances documented in electronic health records.

BACKGROUND: Food allergy prevalence is reported to be increasing, but epidemiological data using patients' electronic health records (EHRs) remain sparse. OBJECTIVE: We sought to determine the prevalence of food allergy and intolerance documented in the EHR allergy module. METHODS: Using allergy data from a large health care organization's EHR between 2000 and 2013, we determined the prevalence of food allergy and intolerance by sex, racial/ethnic group, and allergen group. We examined the prevalence of reactions that were potentially IgE-mediated and anaphylactic. Data were validated using radioallergosorbent test and ImmunoCAP results, when available, for patients with reported peanut allergy. RESULTS: Among 2.7 million patients, we identified 97,482 patients (3.6%) with 1 or more food allergies or intolerances (mean, 1.4 ± 0.1). The prevalence of food allergy and intolerance was higher in females (4.2% vs 2.9%; P < .001) and Asians (4.3% vs 3.6%; P < .001). The most common food allergen groups were shellfish (0.9%), fruit or vegetable (0.7%), dairy (0.5%), and peanut (0.5%). Of the 103,659 identified reactions to foods, 48.1% were potentially IgE-mediated (affecting 50.8% of food allergy or intolerance patients) and 15.9% were anaphylactic. About 20% of patients with reported peanut allergy had a radioallergosorbent test/ImmunoCAP performed, of which 57.3% had an IgE level of grade 3 or higher. CONCLUSIONS: Our findings are consistent with previously validated methods for studying food allergy, suggesting that the EHR's allergy module has the potential to be used for clinical and epidemiological research. The spectrum of severity observed with food allergy highlights the critical need for more allergy evaluations.