Age and Comorbidities Are Associated With Therapeutic Inertia Among Older Adults With Uncontrolled Blood Pressure.

BACKGROUND: Lack of initiation or escalation of blood pressure (BP) lowering medication when BP is uncontrolled, termed therapeutic inertia (TI), increases with age and may be influenced by comorbidities. METHODS: We examined the association of age and comorbidities with TI in 22,665 visits with a systolic BP ≥140 mm Hg and/or diastolic BP ≥90 mm Hg among 7,415 adults age ≥65 years receiving care in clinics that implemented a hypertension quality improvement program. Generalized linear mixed models were used to determine the association of comorbidity number with TI by age group (65-74 and ≥75 years) after covariate adjustment. RESULTS: Baseline mean age was 75.0 years (SD 7.8); 41.4% were male. TI occurred in 79.0% and 83.7% of clinic visits in age groups 65-74 and ≥75 years, respectively. In age group 65-74 years, prevalence ratio of TI with 2, 3-4, and ≥5 comorbidities compared with zero comorbidities was 1.07 (95% confidence interval [CI]: 1.04, 1.12), 1.08 (95% CI: 1.05, 1.12), and 1.15 (95% CI: 1.10, 1.20), respectively. The number of comorbidities was not associated with TI prevalence in age group ≥75 years. After implementation of the improvement program, TI declined from 80.3% to 77.2% in age group 65-74 years and from 85.0% to 82.0% in age group ≥75 years (P < 0.001 for both groups). CONCLUSIONS: TI was common among older adults but not associated with comorbidities after age ≥75 years. A hypertension improvement program had limited impact on TI in older patients.

Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing.

BACKGROUND: Artificial intelligence (AI) tools are more effective if accepted by clinicians. We developed an AI-based clinical decision support system (CDSS) to facilitate vancomycin dosing. This qualitative study assesses clinicians' perceptions regarding CDSS implementation. METHODS: Thirteen semi-structured interviews were conducted with critical care pharmacists, at Mayo Clinic (Rochester, MN), from March through April 2020. Eight clinical cases were discussed with each pharmacist (N = 104). Following initial responses, we revealed the CDSS recommendations to assess participants' reactions and feedback. Interviews were audio-recorded, transcribed, and summarized. RESULTS: The participants reported considerable time and effort invested daily in individualizing vancomycin therapy for hospitalized patients. Most pharmacists agreed that such a CDSS could favorably affect (N = 8, 62%) or enhance (9, 69%) their ability to make vancomycin dosing decisions. In case-based evaluations, pharmacists' empiric doses differed from the CDSS recommendation in most cases (88/104, 85%). Following revealing the CDSS recommendations, we noted 78% (69/88) discrepant doses. In discrepant cases, pharmacists indicated they would not alter their recommendations. The reasons for declining the CDSS recommendation were general distrust of CDSS, lack of dynamic evaluation and in-depth analysis, inability to integrate all clinical data, and lack of a risk index. CONCLUSION: While pharmacists acknowledged enthusiasm about the advantages of AI-based models to improve drug dosing, they were reluctant to integrate the tool into clinical practice. Additional research is necessary to determine the optimal approach to implementing CDSS at the point of care acceptable to clinicians and effective at improving patient outcomes.

CCMapper: An adaptive NLP-based free-text chief complaint mapping algorithm.

OBJECTIVE: Chief complaint (CC) is among the earliest health information recorded at the beginning of a patient's visit to an emergency department (ED). We propose a heuristic methodology for automatically mapping the free-text data into a structured list of CCs. METHODS: A comprehensive structured list categorizing CCs was developed by experienced Emergency Medicine (EM) physicians. Using this list, we developed a natural language processing-based algorithm, referred to as Chief Complaint Mapper (CCMapper), for automatically mapping a CC into the most appropriate category (ies). We trained and validated CCMapper using free-text CC data from the Mayo Clinic ED in Rochester, MN. We developed a consensus-based validation approach to handle both indifferences and disagreements between the two EM physicians who manually mapped a random sample of free-text CCs into categories within the structured list. RESULTS: The kappa statistic demonstrated a high level of agreement (κ = 0.958) between the two physicians with less than 2% human error. CCMapper achieved a total sensitivity of 94.2% with a specificity of 99.8% and F-score of 94.7% on the validation set. The sensitivity of CCMapper when mapping free-text data with multiple CCs was 82.3% with a specificity of 99.1% and total F-score of 82.3%. CONCLUSION: Due to its simplicity, high performance, and capability of incorporating new free-text CC data, CCMapper can be readily adopted by other EDs to support clinical decision making. CCMapper can facilitate the development of predictive models for the type and timing of important events in ED (e.g., ICU admission).