Anesthesia Immutable Registry of Real-time Vital Signs and Waveforms using Blockchain
Figar Gutierrez, A.; Martinez Garbino, J. A.; Burgos, V.; Rajah, T.; Risk, M.; Francisco, R.
Show abstract
Healthcare has become one of the most important emerging application areas of blockchain technology.[1] Although the use of a cryptographic ledger within Anesthesia Information Management Systems (AIMS) remains uncertain. The need for a truly immutable anesthesia record is yet to be established, given that the current AIMS database systems have reliable audit capabilities. Adoption of AIMS has followed Rogers 1962 formulation of the theory of diffusion of innovation. Between 2018 and 2020, adoption was expected to be the 84% of U.S. academic anesthesiology departments.[2] Larger anesthesiology groups with large caseloads, urban settings, and government affiliated or academic institutions are more likely to adopt and implement AIMS solutions, due to the substantial amount of financial resources and dedicated staff to support both the implementation and maintenance that are required. As health care dollars become scarcer, this is the most frequently cited constraint in the adoption and implementation of AIMS.[3] We propose the use of a blockchain database for saving all incoming data from multiparametric monitors at the operating theatre. We present a proof of concept of the use of this technology for electronic anesthesia records even in the absence of an AIMS at site. In this paper we shall discuss its plausibility as well as its feasibility. The Electronic medical records (EMR) in AIMS might contain errors and artifacts that may (or may not) have to be dealt with. Making them immutable is a scary concept. The use of the blockchain for saving raw data directly from medical monitoring equipment and devices in the operating theatre has to be further investigated.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven healthcare research for low-income settings 94%
- Automated Image Transcription for Perinatal Blood Pressure Monitoring Using Mobile Health Technology 93%
- Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality 92%
Similar papers in this journal
- Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space 93%
- ChatGPT-Enhanced ROC Analysis (CERA): A Shiny Web Tool for Finding Optimal Cutoff in Biomarker Analysis 93%
- An interactive retrieval system for clinical trial studies with context-dependent protocol elements 93%
Similar papers in this journal
- An Inexpensive Smartphone-Based Device and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes 92%
- Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence 92%
- Extensive In Silico Analysis of the Functional and Structural Consequences of SNPs in Human ARX Gene 91%
Similar papers in this journal
- Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the OMOP Common Data Model 92%
- Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned 92%
- A Simple Electronic Medical Record System Designed for Research 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.