Abstract P266: The Implementation of Artificial Intelligence Significantly Reduces Door-In Door-Out Times in Primary Care Center Prior to Transfer

Introduction: Viz.ai artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of AI software can reduce the door-in door-out (DIDO) time interval within the primary care center (PSC) prior to transfer to the comprehensive care center (CSC). Methods: We compared the time interval between door-in and door-out for all LVO transfer patients from a single spoke PSC to our CSC prior to (Feb. 2017 to Nov. 2018) and after (Nov. 2018 to June 2020) incorporating Viz.ai. Using a prospectively collected stroke database at a CSC, demographics, DIDO time at PSC, modified Rankin Scale at discharge (mRS dc), mortality rate at discharge, length of stay (LOS) in hospital and neurological ICU, and intracranial hemorrhage rates were examined. Results: There were a total of 63 patients during the study period (average age 69.99 ± 13.72, 55.56% women). We analyzed 28 patients from the pre-AI (average age 71.64 ± 12.28, 46.4%), and 35 patients from the post-AI (average age 68.67 ± 14.88, 62.9% women); see Table 1 for comparison of baseline characteristics and outcomes. Following the implementation of the AI software, the mean DIDO time interval within the PSC significantly improved by 102.3 minutes (226.7 versus 124.4 minutes; p=0.0374); significant reductions were not noted in mRS at discharge, rates of hemorrhage, or mortality. Conclusion: The incorporation of the AI software was associated with a significant improvement in DIDO times within the PSC and may lead to significant improvements in functional outcome and mortality in transfer patients. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

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Abstract P248: CSC Implementation of Artificial Intelligence Software Significantly Improves Door-In to Groin Puncture Time Interval and Recanalization Rates

Stroke ◽

10.1161/str.52.suppl_1.p248 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Ameer E Hassan ◽

Victor M Ringheanu ◽

Laurie Preston ◽

WONDWOSSEN TEKLE

Keyword(s):

Artificial Intelligence ◽

Treatment Time ◽

Care Center ◽

Mass Effect ◽

Comprehensive Care ◽

Time Interval ◽

Vessel Occlusion ◽

Ct Angiogram ◽

Detection Technology ◽

The Mean

Introduction: Viz.ai artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of this AI software can reduce the door-in to groin puncture time interval within the comprehensive care center (CSC) for patients arriving at the CSC for endovascular treatment. Methods: We compared the time interval between door-in to groin puncture for all LVO transfer patients who arrived at our comprehensive care center for approximately two years prior to and after the implementation of the AI software in November of 2018. Using a prospectively collected database at a CSC, demographics, door-in to groin time, modified Rankin Scale at discharge (mRS dc), mortality rate at discharge, length of stay (LOS) in hospital, mass effect, and hemorrhage rates were examined. Results: There were a total of 188 patients during the study period (average age 69.26 ± 14.55, 42.0% women). We analyzed 86 patients from the pre-AI (average age 68.53 ± 13.13, 40.7% women) and 102 patients from the post-AI (average age 69.87 ± 15.75, 43.1% women); see Table 1 for comparison of baseline characteristics and outcomes. Following the implementation of the AI software, the mean door-in to groin puncture time interval within the CSC significantly improved by 86.7 minutes (206.6 vs 119.9 minutes; p < 0.0001); significant improvements were also noted in the rate of good recanalization (mTICI 2B-3) for patients in the post-AI population (p=0.0364). Conclusion: The incorporation of the AI software was associated with a significant improvement in treatment time within the CSC as well as significantly higher rates of good recanalization for patients treated. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

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Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model

Interventional Neuroradiology ◽

10.1177/1591019920953055 ◽

2020 ◽

Vol 26 (5) ◽

pp. 615-622 ◽

Cited By ~ 3

Author(s):

Ameer E Hassan ◽

Victor M Ringheanu ◽

Rani R Rabah ◽

Laurie Preston ◽

Wondwossen G Tekle ◽

...

Keyword(s):

Artificial Intelligence ◽

Length Of Stay ◽

Time Interval ◽

Transfer Time ◽

Vessel Occlusion ◽

Hub And Spoke ◽

Ct Angiogram ◽

Detection Technology ◽

Stroke Center ◽

Transfer Times

Background Recently approved artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of AI software and workflow platform can reduce the transfer time (time interval between CTA at a primary stroke center (PSC) to door-in at a comprehensive stroke center (CSC)). Methods We compared the transfer time for all LVO transfer patients from a single spoke PSC to our CSC prior to and after incorporating AI Software (Viz.ai LVO). Using a prospectively collected stroke database at a CSC, demographics, mRS at discharge, mortality rate at discharge, length of stay (LOS) in hospital and neurological-ICU were examined. Results There were a total of 43 patients during the study period (median age 72.0 ± 12.54 yrs., 51.16% women). Analysis of 28 patients from the pre-AI software (median age 73.5 ± 12.28 yrs., 46.4% women), and 15 patients from the post-AI software (median age 70.0 ± 13.29 yrs., 60.00% women). Following implementation of AI software, median CTA time at PSC to door-in at CSC was significantly reduced by an average of 22.5 min. (132.5 min versus 110 min; p = 0.0470). Conclusions The incorporation of AI software was associated with an improvement in transfer times for LVO patients as well as a reduction in the overall hospital LOS and LOS in the neurological-ICU. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

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Abstract TMP62: Early Experience Utilizing Artificial Intelligence Shows Significant Reduction in Transfer Times and Length of Stay in a Hub and Spoke Model

Stroke ◽

10.1161/str.51.suppl_1.tmp62 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Ameer E Hassan ◽

Victor M Ringheanu ◽

Rani Rabah ◽

Laurie Preston ◽

Adnan I Qureshi

Keyword(s):

Artificial Intelligence ◽

Length Of Stay ◽

Time Interval ◽

Time Of Arrival ◽

Vessel Occlusion ◽

Hub And Spoke ◽

Ct Angiogram ◽

Detection Technology ◽

Stroke Center ◽

Transfer Times

Background: Viz.ai artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of AI software can reduce the time interval between CTA at a primary stroke center (PSC) and arrival time at a comprehensive stroke center (CSC). Methods: We compared time interval between CTA and time of arrival for all LVO transfer patients from a single spoke PSC to our CSC prior to (Feb. 2017 to Nov. 2018) and after (Nov. 2018 to May 2019) incorporating Viz.ai. Using a prospectively collected stroke database at a CSC, demographics, transfer time (CTA time to time of arrival at CSC), modified Rankin Scale at discharge (mRS dc), mortality rate at discharge, length of stay (LOS) in hospital and neurological ICU, and intracranial hemorrhage rates were examined. Results: There were a total of 43 patients during the study period (average age 70.77 ± 12.54 yrs., 51.16% women). Analysis of 28 patients from the pre-Viz.ai (average age 71.64 ± 12.28 yrs., 46.4% women), and 15 patients from the post-Viz.ai (average age 69.13 ± 13.29 yrs., 60.0% women); see Table 1 for comparison of baseline characteristics and outcomes. Following implementation of Viz.ai, CTA time at PSC to time of arrival at CSC was significantly reduced by an average of 66 min. (mean CTA to time of arrival, 171 min. vs 105 min; P= 0.0163); significant reductions were also noted in the overall LOS (9.7 days vs 7.2 days; P= 0.0324) and LOS in the neurological ICU (6.4 days vs 2.9 days; P= 0.0039). Conclusions: The incorporation of Viz.ai was associated with a significant improvement in transfer times for LVO patients as well as a significant reduction in the overall hospital LOS and LOS in the neurological ICU. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

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Safety Education in a Pediatric Primary Care Setting

PEDIATRICS ◽

10.1542/peds.79.5.818 ◽

1987 ◽

Vol 79 (5) ◽

pp. 818-824

Author(s):

Barbara Kelly ◽

Carmen Sein ◽

Paul L. McCarthy

Keyword(s):

Primary Care ◽

Care Center ◽

Intervention Group ◽

Parental Knowledge ◽

Parental Participation ◽

Control Group ◽

Primary Care Center ◽

Safety Education ◽

The Mean ◽

Safety Knowledge

Parents of 171 children coming to the Yale-New Haven Hospital Primary Care Center for their 6-month checkup were randomized into an intervention group (n = 85) and a control group (n = 86). Parents in the intervention group received a three-part individualized course in child safety that required active parental participation. Parts 1, 2, and 3 were given at the 6-month, 9-month, and 12-month well-child visits, respectively. Parents in the control group received routine safety education as provided at well-child visits. The educational phase of the study was completed by 129 families, 65 in the intervention group and 64 in the control group. Safety knowledge, number of hazards in the home, and reported accidents were assessed by a "blinded" community health worker approximately 1 month after the 12-month well-child visit. A total of 109 home visits were made, 55 for the intervention group and 54 for the control group. Parental safety knowledge was assessed based upon pictorial hazard recognition. Of 13 possible hazards, the mean number of hazards recognized by the intervention group parents was 9.4 (n = 55) v 8.4 (n = 50) by the control group parents (t = 2.1, P < .05, two-tailed). A hazard score was determined for each family based on nine possible hazards observed at the home visit. The mean hazard score for the intervention group was 2.4 (n = 55 v 3.0 (n = 54) for the control group (t = 2.4, P < .02, two-tailed). Parentally reported accidents and accidents reported in hospital records were similar for both groups. Results of this study suggest that age-appropriate safety education that is repetitive and individualized and that requires active parental participation results in an increase in parental knowledge and an improvement in certain safety practices.

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A Retrospective Cohort Study of SARS-CoV-2 Infection in Hypertenses Patients in a Primary Care Center

SSRN Electronic Journal ◽

10.2139/ssrn.3605259 ◽

2020 ◽

Author(s):

Esther Hernandez Castilla ◽

Lucia Vallejo Serrano ◽

Monica Saenz Ausejo ◽

Beatriz Pax Sanchez ◽

Katharina Ramrath ◽

...

Keyword(s):

Primary Care ◽

Cohort Study ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Care Center ◽

Primary Care Center

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KNOWLEDGE, ATTITUDE, AND PRACTICE OF PHARMACISTS ABOUT PHARMACEUTICAL CARE FOR DIABETES MELLITUS PATIENTS IN A PRIMARY CARE CENTER IN SAUDI ARABIA

10.21506/j.ponte.2020.3.10 ◽

2020 ◽

Vol 76 (3) ◽

Author(s):

Maha Aldraimly ◽

Sayed Azhar Suliman ◽

Ahmed Ibrahim Nouri ◽

Manahel Mohammed Alshaer ◽

Norah Mohammed Almaghrabi ◽

...

Keyword(s):

Diabetes Mellitus ◽

Primary Care ◽

Saudi Arabia ◽

Pharmaceutical Care ◽

Care Center ◽

Primary Care Center ◽

Attitude And Practice ◽

Knowledge Attitude And Practice

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Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients

Cerebrovascular Diseases ◽

10.1159/000515320 ◽

2021 ◽

pp. 1-6

Author(s):

Jacob R. Morey ◽

Xiangnan Zhang ◽

Kurt A. Yaeger ◽

Emily Fiano ◽

Naoum Fares Marayati ◽

...

Keyword(s):

Artificial Intelligence ◽

Clinical Outcomes ◽

Warning System ◽

Large Vessel ◽

Time Interval ◽

Large Vessel Occlusion ◽

Time Intervals ◽

Vessel Occlusion ◽

Left Behind ◽

Stroke Center

Background and Purpose: Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A computer-aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, a communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment, leading to improved clinical outcomes. Methods: A retrospective analysis of a prospectively maintained database was assessed for patients who presented to a stroke center currently utilizing Viz LVO and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. Time intervals and clinical outcomes were compared for 55 patients divided into pre- and post-Viz cohorts. Results: The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 min [IQR = 12.0] vs. 40.0 min [IQR = 61.0]; p = 0.01) with less variation (p < 0.05) following Viz LVO implementation. The median initial door-to-skin puncture time interval was 25 min shorter in the post-Viz cohort, although this was not statistically significant (p = 0.15). Conclusions: Preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times. This application can serve as an early warning system and a failsafe to ensure that no LVO is left behind.

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SARS-COV-2 INFECTION IN PRIMARY CARE: A SINGLE-CENTERED, RETROSPECTIVE, OBSERVATIONAL STUDY

10.1101/2021.11.30.21267074 ◽

2021 ◽

Author(s):

Pilar Galicia ◽

Juan Jose Gutierrez Cuevas ◽

Fang Fang Chen Chen ◽

Laura Santos Larregola ◽

Alberto Manzanares Briega ◽

...

Keyword(s):

Primary Care ◽

Arterial Hypertension ◽

Clinical Characteristics ◽

Care Center ◽

Case Definition ◽

Living Conditions ◽

Primary Care Center ◽

Predictive Role ◽

Definition Of

Purpose: to describe the clinical characteristics of patients with confirmed SARS-CoV-2 infection in primary care and to analyze the predictive role of different risk factors on prognosis, especially living conditions. Methods: Retrospective longitudinal observational retrospective study by reviewing medical records from a primary care center since March 1 to April 30, 2020. Case definition of confirmed SARS-CoV-2 infection, sociodemographic data, clinical characteristics, comorbidity and living conditions were collected. The statistical analysis consisted in description of the sample, comparison of prognosis groups and analysis of prognostic factors. Results. A sample of 70 patients with confirmed SARS-CoV-2 infection was obtained, with comorbidity mainly related to arterial hypertension, overweight/obesity, hypercholesterolemia, diabetes and chronic pulmonary pathology. Pneumonia was present in 66%. Exitus occurred in 14% of the sample. Factors associated with mortality were advanced age (84 vs 55; p<0.0001), arterial hypertension (78% vs 41%; p=0.040), asthma-COPD (56% vs 13%; p=0.008) and atrial fibrillation (56% vs 5%; p=0.001). Conclusions. The study reflects the clinical practice of a primary care center. This kind of studies are essential to strengthen and reorganize the Health System and to try to anticipate the medium- to long-term consequences of COVID-19 on global health.

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