Abstract TP296: Predicting Cincinnati Prehospital Stroke Scale Components in Emergency Medical Services Patient Care Reports Using Natural Language Processing and Machine Learning

Stroke ◽  
2019 ◽  
Vol 50 (Suppl_1) ◽  
Author(s):  
Ravi Garg ◽  
Christopher T Richards ◽  
Andrew Naidech ◽  
Shyam Prabhakaran
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Care ◽  
Emergency Medical Services ◽  
Language Processing ◽  
Medical Services ◽  
Emergency Medical

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

It’s Time to Talk to Prehospital Providers: Feedback Disparities among Ground-Based Emergency Medical Services Providers and its Impact on Job Satisfaction

2021 ◽  
pp. 1-9
Author(s):  
Sarayna S. McGuire ◽  
Anuradha Luke ◽  
Aaron B. Klassen ◽  
Lucas A. Myers ◽  
Aidan F. Mullan ◽  
...  
Keyword(s):  
Job Satisfaction ◽  
Patient Care ◽  
Emergency Medical Services ◽  
Performance Feedback ◽  
Life Support ◽  
Clinical Care ◽  
Medical Services ◽  
The Past ◽  
Emergency Medical ◽  
Overall Job Satisfaction

Abstract Objective: Performance feedback on clinical care and patient outcomes is a cornerstone of medical education, yet it remains lacking in the prehospital environment. Research seeking to establish the quantity of feedback provided to Emergency Medical Services (EMS) has been limited and studies focused on complimentary feedback or how feedback relates to EMS job satisfaction are lacking. The objectives of this study were to measure the frequency and nature of feedback received by EMS agencies and to identify the importance of receiving feedback as it relates to EMS job satisfaction. Methods: This was an anonymous, survey-based study of twenty-nine Basic Life Support (BLS) and fifteen Advanced Life Support (ALS) agencies located in Southeastern Minnesota (USA). Descriptive statistics and Fisher exact tests were used. The study was deemed exempt by the Mayo Clinic Institutional Review Board. Results: Ninety-four responses were included from nineteen different EMS agencies, including sixty-one (64.9%) paramedics and thirty-three (35.1%) emergency medical technicians (EMTs). One-half of all respondents reported that they had not received any type of feedback in the past 30 days, while another 43.6% of respondents indicated that they had only received feedback one to three times in the same time period. Twenty (60.6%) EMTs reported receiving no feedback in the past 30 days, compared with twenty-seven (44.3%) paramedics (P = .123). Of respondents receiving feedback, 65.9% reported never or rarely receiving positive reinforcing feedback and 60.6% reported never or rarely receiving constructive criticism or feedback regarding something that did not go well with patient care or transport. The majority of respondents were dissatisfied with the quantity (86.1%) and quality (73.4%) of feedback received. An overwhelming majority (93.6%) indicated that feedback on patient care or outcomes was important in influencing their overall job satisfaction. This high importance was maintained across all demographic groups. Conclusion: Within the cohort of survey respondents, a paucity of feedback received by EMS personnel is a source of dissatisfaction for EMS providers. Feedback on patient care strongly relates to overall job satisfaction. These findings suggest system-wide opportunities for structured feedback processes, focusing upon both quality and quantity of delivered feedback, to improve both patient care and staff satisfaction.

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