Identifying Patient Phenotype Cohorts Using Prehospital Electronic Health Record Data

Stemerman, Rachel; Bunning, Thomas; Grover, Joseph M.; Kitzmiller, Rebecca Rutherford; Patel, Mehul D.

doi:10.1080/10903127.2020.1859658

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…We found several emerging, novel use cases of AI in PEC. Firstly, Stemerman et al (112) used clinical notes derived from the EMS to train ML algorithms for patient trial matching, potentially reducing the workload of research nurses and expediting research processes. Also of note is the emerging use of wearable IoT devices.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Chee

Huang

et al. 2023

Preprint

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Introduction: The literature on the use of AI in prehospital emergency care (PEC) settings is scattered and diverse, making it difficult to understand the current state of the field. In this scoping review, we aim to provide a descriptive analysis of the current literature and to visualise and identify knowledge and methodological gaps using an evidence map. Methods: We conducted a scoping review from inception until 14 December 2021 on MEDLINE, Embase, Scopus, IEEE Xplore, ACM Digital Library, and Cochrane Central Register of Controlled Trials (CENTRAL). We included peer-reviewed, original studies that applied AI to prehospital data, including applications for cardiopulmonary resuscitation (CPR), automated external defibrillation (AED), out-of-hospital cardiac arrest, and emergency medical service (EMS) infrastructure like stations and ambulances. Results: The search yielded 4350 articles, of which 106 met the inclusion criteria. Most studies were retrospective (n=88, 83.0%), with only one (0.9%) randomised controlled trial. Studies were mostly internally validated (n=96, 90.6%), and only ten studies (9.4%) reported on calibration metrics. While the most studied AI applications were Triage/Prognostication (n=52, 49.1%) and CPR/AED optimisation (n=26, 24.5%), a few studies reported unique use cases of AI such as patient-trial matching for research and Internet-of-Things (IoT) wearables for continuous monitoring. Out of 49 studies that identified a comparator, 39 reported AI performance superior to either clinicians or non-AI status quo algorithms. The minority of studies utilised multimodal inputs (n=37, 34.9%), with few models using text (n=8), audio (n=5), images (n=1), or videos (n=0) as inputs. Conclusion: AI in PEC is a growing field and several promising use cases have been reported, including prognostication, demand prediction, resource optimisation, and IoT continuous monitoring systems. Prospective, externally validated studies are needed before applications can progress beyond the proof-of-concept stage to real-world clinical settings.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Chee

Huang

et al. 2023

Preprint

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“…First, Stemerman et al. 112 used clinical notes derived from the EMS to train ML algorithms for patient trial matching, potentially reducing the workload of research nurses and expediting research processes by automating workflows. Also of note is the emerging use of wearable IoT devices.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and machine learning in prehospital emergency care: A scoping review

Chee,

Huang

et al. 2023

iScience

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“…Advancements in electronic reporting have improved over the last decade with rural areas obtaining improved connectivity by way of broadband internet. Most PCR data are used for quality assurance and quality control for emergency medical services, but it is increasingly being seen as a viable research dataset [ 17 , 25 , 26 ]. In addition, state-based Emergency Medical Services Bureaus are interested in utilizing the results of research involving PCRs as a way to enhance EMS response.…”

Section: Discussionmentioning

confidence: 99%

The development of a machine learning algorithm to identify occupational injuries in agriculture using pre-hospital care reports

Scott

Hirabayashi

Levenstein

et al. 2021

Health Inf Sci Syst

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Purpose Current injury surveillance efforts in agriculture are considerably hampered by the limited quantity of occupation or industry data in current health records. This has impeded efforts to develop more accurate injury burden estimates and has negatively impacted the prioritization of workplace health and safety in state and federal public health efforts. This paper describes the development of a Naïve Bayes machine learning algorithm to identify occupational injuries in agriculture using existing administrative data, specifically in pre-hospital care reports (PCR). Methods A Naïve Bayes machine learning algorithm was trained on PCR datasets from 2008–2010 from Maine and New Hampshire and tested on newer data from those states between 2011 and 2016. Further analyses were devoted to establishing the generalizability of the model across various states and various years. Dual visual inspection was used to verify the records subset by the algorithm. Results The Naïve Bayes machine learning algorithm reduced the volume of cases that required visual inspection by 69.5 percent over a keyword search strategy alone. Coders identified 341 true agricultural injury records (Case class = 1) (Maine 2011–2016, New Hampshire 2011–2015). In addition, there were 581 (Case class = 2 or 3) that were suspected to be agricultural acute/traumatic events, but lacked the necessary detail to make a certain distinction. Conclusions The application of the trained algorithm on newer data reduced the volume of records requiring visual inspection by two thirds over the previous keyword search strategy, making it a sustainable and cost-effective way to understand injury trends in agriculture.

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Identifying Patient Phenotype Cohorts Using Prehospital Electronic Health Record Data

Cited by 7 publications

References 33 publications

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Artificial intelligence and machine learning in prehospital emergency care: A scoping review

The development of a machine learning algorithm to identify occupational injuries in agriculture using pre-hospital care reports

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