Incorporating RTLS-Based Spatiotemporal Information in Studying Physical Activities of Clinical Staff.

Clinicians and staff who work in intense hospital settings such as the emergency department (ED) are under an extended amount of mental and physical pressure every day. They may spend hours in active physical pressure to serve patients with severe injuries or stay in front of a computer to review patients' clinical history and update the patients' electronic health records (EHR). Nurses on the other hand may stay for multiple consecutive days of 9-12 working hours. The amount of pressure is so much that they usually end up taking days off to recover the lost energy. Both of these extreme cases of low and high physical activities are shown to affect the physical and mental health of clinicians and may even lead to fatigue and burnout.In this study Real-Time location systems (RTLS) are used for the first time, to study the amount of physical activity exerted by clinicians. RTLS systems have traditionally been used in hospital settings for locating staff and equipment, whereas our proposed method combines both time and location information together to estimate the duration, length, and speed of movements within hospital wards such as the ED. It is also our first step towards utilizing non-wearable devices to measure sedentary behavior inside the ED. This information helps to assess the workload on the care team and identify means to reduce the risk of performance compromise, fatigue, and burnout.We used one year worth of raw RFID data that covers movement records of 38 physicians, 13 residents, 163 nurses, 33 staff in the ED. We defined a walking path as the continuous sequences of movements and stops and identified separate walking paths for each individual on each day. Walking duration, distance, and speed, along with the number of steps and the duration of sedentary behavior, are then estimated for each walking path. We compared our results to the values reported in the literature and showed despite the low spatial resolution of RTLS, our non-invasive estimations are closely comparable to the ones measured by Fitbit or other wearable pedometers.Clinical Relevance- Adequate assessment of workload in a dynamic care delivery space plays an important role in ensuring safe and optimal care delivery [7]. Systems capable of measuring physical activities on a continuous basis during daily work can provide precious information for a variety of purposes including automated assessment of sedentary behaviors and early detection of work pressure. Such systems could help facilitate targeted changes in the number of staff, duration of their working shifts leading to a safer and healthier environment for both clinicians and patients.

Usability of a Digital Registry to Promote Secondary Prevention for Peripheral Artery Disease Patients.

OBJECTIVE: To evaluate usability of a quality improvement tool that promotes guideline-based care for patients with peripheral arterial disease (PAD). PATIENTS AND METHODS: The study was conducted from July 19, 2018, to August 21, 2019. We compared the usability of a PAD cohort knowledge solution (CKS) with standard management supported by an electronic health record (EHR). Two scenarios were developed for usability evaluation; the first for the PAD-CKS while the second evaluated standard EHR workflow. Providers were asked to provide opinions about the PAD-CKS tool and to generate a System Usability Scale (SUS) score. Metrics analyzed included time required, number of mouse clicks, and number of keystrokes. RESULTS: Usability evaluations were completed by 11 providers. SUS for the PAD-CKS was excellent at 89.6. Time required to complete 21 tasks in the CKS was 4 minutes compared with 12 minutes for standard EHR workflow (median, P = .002). Completion of CKS tasks required 34 clicks compared with 148 clicks for the EHR (median, P = .002). Keystrokes for CKS task completion was 8 compared with 72 for EHR (median, P = .004). Providers indicated that overall they found the tool easy to use and the PAD mortality risk score useful. CONCLUSIONS: Usability evaluation of the PAD-CKS tool demonstrated time savings, a high SUS score, and a reduction of mouse clicks and keystrokes for task completion compared to standard workflow using the EHR. Provider feedback regarding the strengths and weaknesses also created opportunities for iterative improvement of the PAD-CKS tool.

Provider Survey on Automated Clinical Decision Support for Cardiovascular Risk Assessment.

OBJECTIVE: To investigate provider opinions regarding a clinical decision support (CDS) system for cardiovascular risk assessment and for the creation of a replacement system. METHODS: From March to April 2018, an invitation letter with a link to a self-administered web-based survey was sent via e-mail to 279 providers with primary appointment in the Department of Cardiovascular Medicine, Mayo Clinic, Rochester. The e-mail was sent to providers on March 8, 2018 and the survey closed on April 16, 2018. RESULTS: One hundred providers responded to the survey yielding an overall response rate of 35.8%. Of these, 52 (52%) indicated they had used the cardiovascular (CV) risk profile CDS system and were classified as users and prompted to continue the survey. Among users, 42 (80.8%) indicated use of the CDS was either important (25; 48.1%) or very important (17; 32.7%) in their clinical practice; 45 (86.5%) responded that the system was very easy (17; 32.7%) or easy (28; 53.8%) to use. In addition, 48 (96.0%) users indicated that the CV risk profile supported their thought process at the point-of-care; 47 (97.9%) users indicated similar functionalities should be implemented into the new electronic health record system and 41 (85.4%) users reported new functionalities should also be incorporated. CONCLUSIONS: For most users, the CDS system was easy to use and supported clinical thought process at the point-of-care. Users also felt their practice was supported and should continue to be supported by CDS systems providing individualized patient information at the point-of-care.

Provider Survey on Automated Clinical Decision Support System for Cardiovascular Risk Assessment.

Despite progress made in establishing primary and secondary preventive strategies for cardiovascular diseases, there are significant gaps between guideline recommended strategies and implementation of recommendations in practice. A clinical decision support (CDS) system entitled CV Risk Profile was developed at Mayo Clinic Rochester as a targeted solution for this gap in preventive cardiovascular care. The system remained in use for 10 years until it became non-functional in 2018 during transition to a new electronic health record (EHR). This study investigated provider opinions regarding the cardiovascular disease CDS system while it was still in operation, to determine if there exists a provider reported need for a similar system to be developed for use within the new EHR.

Colonoscopy surveillance for high risk polyps does not always prevent colorectal cancer.

AIM: To determine the frequency and risk factors for colorectal cancer (CRC) development among individuals with resected advanced adenoma (AA)/traditional serrated adenoma (TSA)/advanced sessile serrated adenoma (ASSA). METHODS: Data was collected from medical records of 14663 subjects found to have AA, TSA, or ASSA at screening or surveillance colonoscopy. Patients with inflammatory bowel disease or known genetic predisposition for CRC were excluded from the study. Factors associated with CRC developing after endoscopic management of high risk polyps were calculated in 4610 such patients who had at least one surveillance colonoscopy within 10 years following the original polypectomy of the incident advanced polyp. RESULTS: 84/4610 (1.8%) patients developed CRC at the polypectomy site within a median of 4.2 years (mean 4.89 years), and 1.2% (54/4610) developed CRC in a region distinct from the AA/TSA/ASSA resection site within a median of 5.1 years (mean 6.67 years). Approximately, 30% (25/84) of patients who developed CRC at the AA/TSA/ASSA site and 27.8% (15/54) of patients who developed CRC at another site had colonoscopy at recommended surveillance intervals. Increasing age; polyp size; male sex; right-sided location; high degree of dysplasia; higher number of polyps resected; and piecemeal removal were associated with an increased risk for CRC development at the same site as the index polyp. Increasing age; right-sided location; higher number of polyps resected and sessile endoscopic appearance of the index AA/TSA/ASSA were significantly associated with an increased risk for CRC development at a different site. CONCLUSION: Recognition that CRC may develop following AA/TSA/ASSA removal is one step toward improving our practice efficiency and preventing a portion of CRC related morbidity and mortality.

Innovative Informatics Approaches for Peripheral Artery Disease: Current State and Provider Survey of Strategies for Improving Guideline-Based Care.

OBJECTIVE: To quantify compliance with guideline recommendations for secondary prevention in peripheral artery disease (PAD) using natural language processing (NLP) tools deployed to an electronic health record (EHR) and investigate provider opinions regarding clinical decision support (CDS) to promote improved implementation of these strategies. PATIENTS AND METHODS: Natural language processing was used for automated identification of moderate to severe PAD cases from narrative clinical notes of an EHR of patients seen in consultation from May 13, 2015, to July 27, 2015. Guideline-recommended strategies assessed within 6 months of PAD diagnosis included therapy with statins, antiplatelet agents, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and smoking abstention. Subsequently, a provider survey was used to assess provider knowledge regarding PAD clinical practice guidelines, comfort in recommending secondary prevention strategies, and potential role for CDS. RESULTS: Among 73 moderate to severe PAD cases identified by NLP, only 12 (16%) were on 4 guideline-recommended strategies. A total of 207 of 760 (27%) providers responded to the survey; of these 141 (68%) were generalists and 66 (32%) were specialists. Although 183 providers (88%) managed patients with PAD, 51 (25%) indicated they were uncomfortable doing so; 138 providers (67%) favored the development of a CDS system tailored for their practice and 146 (71%) agreed that an automated EHR-derived mortality risk score calculator for patients with PAD would be helpful. CONCLUSION: Natural language processing tools can identify cases from EHRs to support quality metric studies. Findings of this pilot study demonstrate gaps in application of guideline-recommended strategies for secondary risk prevention for patients with moderate to severe PAD. Providers strongly support the development of CDS systems tailored to assist them in providing evidence-based care to patients with PAD at the point of care.

Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease.

Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real-time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5-year follow-up. The c-statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74-0.78), and the c-statistic across 10 cross-validation data sets was 0.75 (95% CI, 0.73-0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21-0.58]; intermediate-high: hazard ratio, 2.98 [95% CI, 2.37-3.74]; high: hazard ratio, 8.44 [95% CI, 6.66-10.70], all P<0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and ß estimates. Big data infrastructure enabled deployment of the real-time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real-time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real-time risk calculator deployed at the point of care.