A Novel Interdisciplinary Iterative Approach for Optimizing the Electronic Health Record to Improve Perioperative Efficiency.

OBJECTIVE: We present a holistic perioperative optimization approach led by a CI team with the goal to optimize the workflow within our EHR, improve operative room metrics and user satisfaction. SUMMARY OF BACKGROUND DATA: The EHR has become integral to perioperative care. Many approaches are utilized to improve performance including systems-based approaches, process redesign, lean methodology, checklists, root cause analysis, and parallel processing. Although most reports describe strategies improving day or surgery productivity, few include perioperative interventions to improve efficiencies. METHODS: An interdisciplinary CI team consisting of clinicians, informatics specialists, and analysts spent 6 weeks assessing users and optimizing all perioperative areas (scheduling, day of surgery, postop discharge/admission). Elbow-to-elbow retraining and simultaneous content development was performed utilizing an Agile workflow process optimization with the Scrum framework. This iterative approach averaged 1 week from build to change implementation. Pre/post optimization surveys were sent. RESULTS: Two hundred forty-two perioperative enhancements were completed. While most impacted documentation, all areas were enhanced including billing, reporting, registration, device integration, scheduling, central supply, and so on. FCOTS improved from <70% to >85% and total delay was halved. These parameters were consistently sustained for over 1 year after the 6-week optimization. While only 5% of pre-optimization users agreed to proficiency in the EHR system, this improved to 70% post-optimization. Furthermore, EHR confidence and acceptance improved from 40% to 90%. CONCLUSIONS: To improve workflow efficiency, all who contribute to the perioperative process must be assessed. This IT driven initiative resulted in improved FCOTS, perioperative workflows, and user satisfaction.

Concordance between clinical outcomes in the Systolic Blood Pressure Intervention Trial and in the electronic health record.

BACKGROUND: Randomized trials are the gold standard for generating clinical practice evidence, but follow-up and outcome ascertainment are resource-intensive. Electronic health record (EHR) data from routine care can be a cost-effective means of follow-up, but concordance with trial-ascertained outcomes is less well-studied. METHODS: We linked EHR and trial data for participants of the Systolic Blood Pressure Intervention Trial (SPRINT), a randomized trial comparing intensive and standard blood pressure targets. Among participants with available EHR data concurrent to trial-ascertained outcomes, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for EHR-recorded cardiovascular disease (CVD) events, using the gold standard of SPRINT-adjudicated outcomes (myocardial infarction (MI)/acute coronary syndrome (ACS), heart failure, stroke, and composite CVD events). We additionally compared the incidence of non-CVD adverse events (hyponatremia, hypernatremia, hypokalemia, hyperkalemia, bradycardia, and hypotension) in trial versus EHR data. RESULTS: 2468 SPRINT participants were included (mean age 68 (SD 9) years; 26% female). EHR data demonstrated ≥80% sensitivity and specificity, and ≥ 99% negative predictive value for MI/ACS, heart failure, stroke, and composite CVD events. Positive predictive value ranged from 26% (95% CI; 16%, 38%) for heart failure to 52% (95% CI; 37%, 67%) for MI/ACS. EHR data uniformly identified more non-CVD adverse events and higher incidence rates compared with trial ascertainment. CONCLUSIONS: These results support a role for EHR data collection in clinical trials, particularly for capturing laboratory-based adverse events. EHR data may be an efficient source for CVD outcome ascertainment, though there is clear benefit from adjudication to avoid false positives.

Correlative analysis of wastewater trends with clinical cases and hospitalizations through five dominant variant waves of COVID-19.

Wastewater-based epidemiology (WBE) has been utilized to track community infections of Coronavirus Disease 2019 (COVID-19) by detecting RNA of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), within samples collected from wastewater. The correlations between community infections and wastewater measurements of the RNA can potentially change as SARS-CoV-2 evolves into new variations by mutating. This study analyzed SARS-CoV-2 RNA, and indicators of human waste in wastewater from two sewersheds of different scales (University of Miami (UM) campus and Miami-Dade County Central District wastewater treatment plant (CDWWTP)) during five internally defined COVID-19 variant dominant periods (Initial, Pre-Delta, Delta, Omicron and Post-Omicron wave). SARS-CoV-2 RNA quantities were compared against COVID-19 clinical cases and hospitalizations to evaluate correlations with wastewater SARS-CoV-2 RNA. Although correlations between documented clinical cases and hospitalizations were high, prevalence for a given wastewater SARS-CoV-2 level varied depending upon the variant analyzed. The correlative relationship was significantly steeper (more cases per level found in wastewater) for the Omicron-dominated period. For hospitalization, the relationships were steepest for the Initial wave, followed by the Delta wave with flatter slopes during all other waves. Overall results were interpreted in the context of SARS-CoV-2 virulence and vaccination rates among the community.

Factors to Effective Telemedicine Visits During the COVID-19 Pandemic: Cohort Study.

BACKGROUND: With COVID-19 there was a rapid and abrupt rise in telemedicine implementation often without sufficient time for providers or patients to adapt. As telemedicine visits are likely to continue to play an important role in health care, it is crucial to strive for a better understanding of how to ensure completed telemedicine visits in our health system. Awareness of these barriers to effective telemedicine visits is necessary for a proactive approach to addressing issues. OBJECTIVE: The objective of this study was to identify variables that may affect telemedicine visit completion in order to determine actions that can be enacted across the entire health system to benefit all patients. METHODS: Data were collected from scheduled telemedicine visits (n=362,764) at the University of Miami Health System (UHealth) between March 1, 2020 and October 31, 2020. Descriptive statistics, mixed effects logistic regression, and random forest modeling were used to identify the most important patient-agnostic predictors of telemedicine completion. RESULTS: Using descriptive statistics, struggling telemedicine specialties, providers, and clinic locations were identified. Through mixed effects logistic regression (adjusting for clustering at the clinic site level), the most important predictors of completion included previsit phone call/SMS text message reminder status (confirmed vs not answered) (odds ratio [OR] 6.599, 95% CI 6.483-6.717), MyUHealthChart patient portal status (not activated vs activated) (OR 0.315, 95% CI 0.305-0.325), provider's specialty (primary care vs medical specialty) (OR 1.514, 95% CI 1.472-1.558), new to the UHealth system (yes vs no) (OR 1.285, 95% CI 1.201-1.374), and new to provider (yes vs no) (OR 0.875, 95% CI 0.859-0.891). Random forest modeling results mirrored those from logistic regression. CONCLUSIONS: The highest association with a completed telemedicine visit was the previsit appointment confirmation by the patient via phone call/SMS text message. An active patient portal account was the second strongest variable associated with completion, which underscored the importance of patients having set up their portal account before the telemedicine visit. Provider's specialty was the third strongest patient-agnostic characteristic associated with telemedicine completion rate. Telemedicine will likely continue to have an integral role in health care, and these results should be used as an important guide to improvement efforts. As a first step toward increasing completion rates, health care systems should focus on improvement of patient portal usage and use of previsit reminders. Optimization and intervention are necessary for those that are struggling with implementing telemedicine. We advise setting up a standardized workflow for staff.

Demographics associated with US healthcare disparities are exacerbated by the telemedicine surge during the COVID-19 pandemic.

INTRODUCTION: As coronavirus disease 2019 (COVID-19) hit the US, there was widespread and urgent implementation of telemedicine programs nationwide without much focus on the impact on patient populations with known existing healthcare disparities. To better understand which populations cannot access telemedicine during the coronavirus disease 2019 pandemic, this study aims to demographically describe and identify the most important demographic predictors of telemedicine visit completion in an urban health system. METHODS: Patient de-identified demographics and telemedicine visit data (N = 362,764) between March 1, 2020 and October 31, 2020 were combined with Internal Revenue Service 2018 individual income tax data by postal code. Descriptive statistics and mixed effects logistic regression were used to determine impactful patient predictors of telemedicine completion, while adjusting for clustering at the clinical site level. RESULTS: Many patient-specific demographics were found to be significant. Descriptive statistics showed older patients had lower rates of completion (p < 0.001). Also, Hispanic patients had statistically significant lower rates (p < 0.001). Overall, minorities (racial, ethnic, and language) had decreased odds ratios of successful telemedicine completion compared to the reference. DISCUSSION: While telemedicine use continues to be critical during the coronavirus disease 2019 pandemic, entire populations struggle with access-possibly widening existing disparities. These results contribute large datasets with significant findings to the limited research on telemedicine access and can help guide us in improving telemedicine disparities across our health systems and on a wider scale.