A clinical decision support system (CDSS) is a computerized application that helps clinicians detect and prevent untoward clinical events such as drug interactions, errors of omission, and trends in symptomatology. A CDSS in healthcare usually is built around an alerting system based on rules of logic. The alerting system of a CDSS can notify clinicians immediately on clinical data entry, or it can generate alerts over time after relating data from multiple sources. A CDSS for nurses and patients offers immediate benefits for nurses and patients by detecting potential drug-laboratory and drug-drug combinations and impending pharmacologic complications, monitoring microbiology results, and helping nurses relate symptoms to pharmacology and medication side effects. Other benefits include savings in time and money and reductions in morbidity and mortality. A CDSS presents an opportunity for nursing informatics and critical care nursing to collaborate for the benefit of the patient and the profession.
Background Ambulatory ECG (AECG) monitoring is pivotal to the diagnosis of arrhythmias and can be performed with near “real‐time” notification of abnormalities. There are limited data on the relative benefit of real‐time monitoring compared with traditional Holter monitoring. Methods and Results This is a retrospective observational analysis of University of Utah Health patients who underwent ambulatory ECG studies from 2010 to 2022. The study cohort was stratified by patients with an ambulatory ECG that provides real‐time event notification (non‐Holter) versus those who do not (Holter). The outcomes were cardiac implantable electronic device procedure, ablation procedure, emergency department/hospitalization visit, and initiation of anticoagulation out to 6 months. We identified 20 259 patients, 16 650 with non‐Holter studies and 3609 with Holter studies. Holter patients were younger (mean 52 versus 55, P <0.001), more often women (60.2% versus 57%, P <0.001), and had lower mean CHADS 2 ‐VA 2 Sc scores (1.7 versus 2.1, P <0.001). The median time to ablation procedure was 74 versus 72 ( P =0.5), for Holter versus non‐Holter, respectively. Median days to new cardiac implantable electronic device implantation was 54 days versus 52 ( P =0.6); initiation of anticoagulation among patients not already treated was 42 versus 31 days ( P =0.03). Time to first emergency department visit or hospitalization was 63 versus 57 ( P =0.6). In multivariable models, there were no significant differences in time to intervention between Holter and non‐Holter for each outcome. Conclusions Real‐time monitoring demonstrates mixed results in terms of reducing time to intervention, with the significant benefit limited to oral anticoagulation initiation. It is time to revisit clinical scenarios where real‐time ambulatory monitoring may not improve health care efficiency.
International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)–related symptoms. Their accuracy is unknown, which could affect downstream analyses.
Objective
To compare the performance of fever-, cough-, and dyspnea-specificICD-10codes with medical record review among patients tested for COVID-19.
Design, Setting, and Participants
This cohort study included patients who underwent quantitative reverse transcriptase–polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 at University of Utah Health from March 10 to April 6, 2020. Data analysis was performed in April 2020.
Main Outcomes and Measures
The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) ofICD-10codes for fever (R50*), cough (R05*), and dyspnea (R06.0*) were compared with manual medical record review. Performance was calculated overall and stratified by COVID-19 test result, sex, age group (<50, 50-64, and >64 years), and inpatient status. Bootstrapping was used to generate 95% CIs, and Pearson χ2tests were used to compare different subgroups.
Results
Among 2201 patients tested for COVD-19, the mean (SD) age was 42 (17) years; 1201 (55%) were female, 1569 (71%) were White, and 282 (13%) were Hispanic or Latino. The prevalence of fever was 66% (1444 patients), that of cough was 88% (1930 patients), and that of dyspnea was 64% (1399 patients). For fever, the sensitivity ofICD-10codes was 0.26 (95% CI, 0.24-0.29), specificity was 0.98 (95% CI, 0.96-0.99), PPV was 0.96 (95% CI, 0.93-0.97), and NPV was 0.41 (95% CI, 0.39-0.43). For cough, the sensitivity ofICD-10codes was 0.44 (95% CI, 0.42-0.46), specificity was 0.88 (95% CI, 0.84-0.92), PPV was 0.96 (95% CI, 0.95-0.97), and NPV was 0.18 (95% CI, 0.16-0.20). For dyspnea, the sensitivity ofICD-10codes was 0.24 (95% CI, 0.22-0.26), specificity was 0.97 (95% CI, 0.96-0.98), PPV was 0.93 (95% CI, 0.90-0.96), and NPV was 0.42 (95% CI, 0.40-0.44).ICD-10code performance was better for inpatients than for outpatients for fever (χ2 = 41.30;P < .001) and dyspnea (χ2 = 14.25;P = .003) but not for cough (χ2 = 5.13;P = .16).
Conclusions and Relevance
These findings suggest thatICD-10codes lack sensitivity and have poor NPV for symptoms associated with COVID-19. This inaccuracy has implications for any downstream data model, scientific discovery, or surveillance that relies on these codes.
Abstract Aims Incorporating patient-reported outcomes (PROs) into routine care of atrial fibrillation (AF) enables direct integration of symptoms, function, and health-related quality of life (HRQoL) into practice. We report our initial experience with a system-wide PRO initiative among AF patients. Methods and results All patients with AF in our practice undergo PRO assessment with the Toronto AF Severity Scale (AFSS), and generic PROs, prior to electrophysiology clinic visits. We describe the implementation, feasibility, and results of clinic-based, electronic AF PRO collection, and compare AF-specific and generic HRQoL assessments. From October 2016 to February 2019, 1586 unique AF patients initiated 2379 PRO assessments, 2145 of which had all PRO measures completed (90%). The median completion time for all PRO measures per visit was 7.3 min (1st, 3rd quartiles: 6, 10). Overall, 38% of patients were female (n = 589), mean age was 68 (SD 12) years, and mean CHA2DS2-VASc score was 3.8 (SD 2.0). The mean AFSS symptom score was 8.6 (SD 6.6, 1st, 3rd quartiles: 3, 13), and the full range of values was observed (0, 35). Generic PROs of physical function, general health, and depression were impacted at the most severe quartiles of AF symptom score (P < 0.0001 for each vs. AFSS quartile). Conclusion Routine clinic-based, PRO collection for AF is feasible in clinical practice and patient time investment was acceptable. Disease-specific AF PROs add value to generic HRQoL instruments. Further research into the relationship between PROs, heart rhythm, and AF burden, as well as PRO-guided management, is necessary to optimize PRO utilization.
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