We propose to analyze the intraoperative scenario in a reductionist way by considering it as a cybernetic model (Novikov, 2016).

The set of the anesthetized patient being controlled by the anesthesiologist can be interpreted as a control system consisting of four basic components:

1) A controlled component, represented by the patient, which can be considered a complex dynamical system (Ladyman et al., 2013).

We consider a “safe performance” to be the active maintenance of the patient’s physiological and biochemical parameters within ranges considered normal by scientific consensus (Joyce et al., 2017). To achieve this, anesthesiologists must take effective action to recover parameters when they deviate from these reliable limits. Lack of timely correction of such deviations can threaten the integrity of the patient and generate potential harm.

The concept of “harm” is broad and can range from general eventualities such as hypoxia, hypoperfusion, pain, acidosis or hypothermia; to specific damage caused by burns, cuts or compressions. Harm can be reversible, irreversible (generating sequelae or disability) or even cause death.

As a controller, the role of the anesthesiologist is to preserve situational awareness (McIlvaine, 2007; Schulz et al., 2013; Schulz et al., 2015) to efficiently prevent harm and ensure patient safety during the entire anesthetic procedure until self-control is regained or exerted by another controller (such as an intensive care service).

Similarly to pilots, anesthesiologists are in charge of detecting anomalies and resolving complications efficiently (Toff, 2010). To understand the causes of suboptimal performance and undesired outcomes during intraoperative anesthesia, failure scenarios should be outlined. The term “anomaly” refers to any actual or potential inconsistency, irregularity or deviation of parameters which could expose patients to risk or harm.

From the controller perspective, suboptimal performance can be framed in four main failure scenarios:

A) Detection Failure : an anomaly is not detected or detection is delayed.

The root causes of main failure scenarios can be traced to:

1-Controller Absence

Within this framework, causes of suboptimal record rebuilding must be also recognized:

A) Incomplete/Interrupted Registration

Anesthesia records should enable easy contextualization of any outcome of the control system. Although record immutability is an intimidating concept, technology should protect professionals and institutions from liability risks by providing objective evidence of compliance with quality and safety standards (Feldman, 2004). Genuine commitment to patient safety must involve full transparency for patients and society. Record rebuilding should be guaranteed by a solid tamper-resistant architecture.

Paper-based anesthesia records present several disadvantages: difficult legibility of handwritten information, asynchrony between measurement and data registration, susceptibility to inaccuracies, loss of relevant data, and user distraction from the environment surveillance (Reich et al., 2000; van Schalkwyk et al., 2011; Edwards et al., 2013; Anderson and Merry, 2015). Paper sheets are easy to tamper, their authenticity relies on sensitive mechanisms such as handwritten signatures or classic stamping. Finally, being physical objects, they are prone to damage or loss.

Prevailing electronic anesthesia records provide many advantages over handwritten registers (Ehrenfeld and Rehman, 2011), however, some aspects of their design can still be improved. For example, data is locally stored on the sampling devices or in centralized institutional networks, which can lead to data vulnerability or access limitation. Authentication relies on local mechanisms such as passwords for the monitor hardware or for local networks. User profiles can generally be created readily and can also be issued and withdrawn by individuals or centralized administrations, providing a technologically weak mechanism for validating user legitimacy.

Current record traceability and timestamping do not depend on public networks, nor do they count on mechanisms to generate public proof of existence or immutability. Data provenance is not endorsed by network-based geolocation.

Finally, there is a general lack of compatibility and interoperability between electronic systems for the integration of data in real time.

In order to optimize the “control process”, the quality of the generated data should be maximized. An ideal record system should provide unbiased data and ensure the immutability of information, precluding the influence of particular interests of individuals or institutions. In addition, mechanisms to generate evidence about the performance of the system components and their interactions must be provided by the architecture itself.

We propose a model based on the application of open-source hardware and software for the registration of sensor time series as a fundamental principle for case analysis and reconstructions. Decentralization and technological independence ensure an impartial and immutable data outflow. The records integrate real-time information on the spatio-temporal relation between the patient and the anesthesiologist, generating evidence of the entire anesthesia control system. To enhance the interpretation of the time series, manual incorporation of supplemental data related to the anesthesia case is invited by the system.

A global anesthesiologist ecosystem can be assembled over a record technology based on specific features to ensure scientific rigor in the data generation process. While these characteristics define the system as a whole, we divide them into three main categories for an intelligible exposition:

a) Features of the data generation process

We introduce a model to resolve current issues and comply with the proposed features. In the following section we describe hardware components and their interactions (Figure 1). We outline the aim of the system software and the integration of blockchains with decentralized storage systems. We also describe methods for generating, updating and retrieving anesthesia records.

The descriptions refer to what the authors consider to be the best system configuration, but since various possibilities in component design and connectivity are compatible with the main objective, alternative implementations are mentioned.

We envision that this proof of concept should be initially implemented as a physically independent system from the anesthesia ventilator and multiparameter monitor. This is because it is a technically simpler way to evaluate its performance and adoption during early stages. It should be noted that the initial implementation as a stand-alone system can contribute to safety by being available as a second multiparameter monitor in the operating room, providing backup in complex scenarios where sensor malfunction, disconnection or interference may hamper decision-making processes.

The device consists of a module that contains an array of sockets into which cables from different sensors are connected. Its function is to collect data from sensors and deliver it to a dedicated computer device that broadcasts the data streams to remote storage systems. To provide a mechanism for tracking and measuring instrument performance, the sensors are equipped with an electronic identifier through which the system is able to register their specifications such as model, developer, serial number and time of use. Since the system is intended to be suited for research purposes, several sockets may be present for the same type of sensors.

Each channel receives a specific type of input consisting of biosignals from patients or data from electromechanical devices connected to them. These may include signals from pulse oximetry, electrocardiography, noninvasive blood pressure, invasive pressure, electroencephalography (EEG), near-infrared spectrometry, temperature, and data from the respiratory circuit (capnography, flow, volume, pressure, and gas analysis). Signals that require invasive instruments, such as intravascular sensors, can be electronically split and delivered to the sampling unit and main multi-parameter monitors from a unique transducer.

Data streams are preferably transferred to the computer device via wireless connectivity in order to prevent cables interfering with staff circulation in the operating room.

To maximize electrical safety for patients, this device is ideally powered by rechargeable batteries.

This module is a personal specific-application computer, equipped with a screen and input devices such as keyboard, moused, knobs and buttons for easy user interaction. It contains the front-end software that allows the anesthesiologist to log into the system, authenticate, generate case registers, consult them and introduce supplementary information to the records.

This device receives the sensors’ time series from the sampling unit and transmits data streams to the remote storage systems via Internet connection. The sampled data is displayed in real time enabling users to interact with it through visualization tools and analytics software.

It is equipped with a fingerprint reader for generating biometric signatures during the anesthesia procedure. Geolocation and synchronization with universal coordinated time (UTC) is established through Internet connectivity.

A set of antennas built into the unit allows bidirectional connectivity with other devices maintaining constant communication with the wearable transponder ² . In addition, an integrated very short-range radio frequency identification system (RFID) allows the detection of the wearable device in close proximity, providing a mechanism to validate operations on the computer device performed by the transponder holder.

This device consists of a unique personal smart bracelet equipped with accelerometers, fingerprint reader, vibration motor, sound emitting system, microphone and a magnetic clasp with an electronic contact. Its function is to provide the anesthesiologist with a self-authentication mechanism and generate evidence on the active vigilance of the anesthetized patient. This is done by measuring irregular acceleration patterns on the wearable and estimating the distance between this device, the computer, and the sampling unit through the wireless connectivity.

A very short-range RFID verifies the close proximity of the wearable during the computer device operation.

Bidirectional communication enables the user to remotely interact with the computer device through buttons and a touch-sensitive scrolling surface on the smart bracelet. The device is also able to receive signals from the computer which can trigger vibrations or sound alerts on it.

The built-in microphone allows the incorporation of voice notes to the records. Although the system is not conceived to incorporate personally identifiable information from patients, brief timestamped voice statements from individuals consenting to have their data recorded, can be integrated into the anesthesia record to provide a biometric proof of voluntary acceptance for their data processing.

This is a complementary device designed to synchronize third-party video recordings with the sampled data. It provides a precise mechanism to create multimedia content integrated with anesthesia data streams.

The device consists of an array of lasers which are pointed toward the surgical field or a site of interest. It is connected to the sampling unit or to the computer device and synchronized with the system´s native chronometer. A variable dot pattern is generated intermittently and projected by the lasers in synchrony with the time series clock. The dot pattern code embedded within the record, provides a way for any video content that includes the dynamic pattern in the filmed plane to be synchronized with the data streams by matching the visual time pattern with the one in the time series (Figures 2, 3).

The network of peer-to-peer specific-application computer nodes, integrates into a governance layer. This structure exerts parallel control over the different system components: storage services, blockchains, and outsourced computational networks (Figures 4, 5). Its functions are:

Data Management:

- Verify the legitimacy of its users

Coordination for the Participation of Nodes:

- Conduct the voting system for: network consensus, use of the decentralized-treasury funds, improvement proposals, community consultation, insurance resolutions.

System Administration:

- System maintenance

An inherent value of the system is that as a result of its processes, anesthesia records (which are encrypted and certified by the system) are stored and always available through content addressable storage, evidencing their existence on a public blockchain.

This implies that even if the system architecture ceases to exist, the records and the evidence of their generation and immutability would remain available. In this hypothetical case, since the system´s treasury is aimed to cover the costs for the pinning services of the records though smart contracts; these would remain pinned for a considerable period, allowing anesthesiologists and patients to download their records and pin them themselves to ensure their permanent availability.

Access to the content of the records, and thus the preservation of their privacy, would rely on the holders of their respective decryption keys. Interestingly, techniques for content erasure have been proposed for CAS in order to meet the “right to be forgotten” requirement for personal data protection regimes (Politou et al., 2020).

While the application of a second set of sensors connected to the patient could be considered as an over-engineering approach, it should be understood that this is implemented in this way to allow the gradual operation and testing of the concept without affecting the validated monitors of the operating room. We foresee that, if adopted, some components of future versions of the system could be simplified or even dispensed with. For example, the sampling unit could be reduced to a small module which may capture raw data wirelessly from multiparameter monitors and anesthesia machines. These in turn, should be able to transmit their raw data and the technical identifiers of their components. The wireless sampling unit could be further integrated into the specific application computer device, which may become a main multiparameter monitor in the operating room.

The proposed system architecture addresses the aforementioned features in the following way:

1) Automaticity: Sampling and recording is done automatically, reducing distractions and allowing anesthesiologists to focus on patient care. Sensor measurements are transmitted directly avoiding the inaccuracies of manual data transcriptions.

It should be noted that biometrically generated information is enclosed within the encrypted records and is not made publicly available as part of the metadata.

8) Traceability: Data is recorded with a certification of their respective geolocation and UTC timestamps which are determined through the network connectivity. This information is also embedded and publicly available on the blockchain register.

Wearable devices provide trainees with a mechanism to record and certify their actively supervised anesthesia cases. Data collected throughout the anesthesiologist career can support an updated interoperable register to provide proof of training and proficiency worldwide (Simpao et al., 2011). Similarly professionals who supervise anesthesiologists in training can provide objective evidence of their involvement and expertise as instructors through their personal records.

11) Data Immutability : Content hashes certify the immutability of records. A persistent data structure ensures access to historical information by adding corrections and new data without overwriting the original entries. Modifications to records are incorporated in a layered structure, on top of previous data, so that information from all versions is preserved.

Alternative third-party sensors may be compatible with the system as long as they are based on open-source technology and provide electronic instrument identifiers.

15) Bidirectional Communication Support for Wearable Devices: Wearable transponders maintain two-way communication with the computer device enabling remote operation, self-authentication and voice recording.

Users can be alerted through the wearable via vibration or sound whenever the system interprets measurements consistent with situations of risk for the patient. The wearable can alert the user autonomously even when it has no connection to the computer device.

Short-range RFID validates computer manipulations carried out in close proximity to the transponder worn on the wrist of the user.

Information generated by the wearable is included in the anesthesia record.

16) Anti-Sybil Backed System: Blockchain-based “unique human identity systems” can be integrated by linking blockchain accounts to the user profile or by incorporating signed messages within the anesthesia record. As an example of the latter case, signatures can be generated with the private key of a personal identity blockchain address and embodied in the record. Alternatively, a system dedicated address, linked to the user´s personal identity address can be utilized to generate signatures or ZKP.

The scientifically rigorous data generated by the system is technically ideal for feeding artificial neural networks. In this framework, it should be noted that the system could leverage the application of “federated learning” and “federated analytics”: machine learning techniques that train algorithms for generating models and predictions across multiple decentralized nodes without the need to exchange or centralize data sets (Rieke et al., 2020; Short et al., 2022). These may become essential implementations in the field of anesthesiology, as they would address critical issues concerning data privacy, data security, and access rights while taking advantage of distributed computation.

Interestingly, distributed learning may coexist with decentralized storage as nodes could train their local data while permanently storing it on politically decentralized storage systems. In this way, each node could retain governance rights over its generated data while preserving a mechanism for the system community to access it through the consensus of the decentralized governance layer.

Since reliance on data is essential to generate accurate models; the need for a governance layer to ensure data integrity and filter out unacceptable nodes seems critical.

Live-streamed data can be used for modeling, calibrating and auditing technologies based on artificial intelligence such as automatic closed-loop infusion systems.

23) Compatibility and Interoperability for Supplementary Data Incorporation: Users are incentivized to generate rigorous and exhaustive records as these are cataloged according to their completeness. Datasets incorporating accurate supplementary information are expected to be valuable for researchers from different fields. Records utilized for scientific research can be publicly certified by the system. These can be used to establish a reputation of users as research contributors.

Users are able to create their own applications and share them with the system community for testing. Innovative software tools that prove useful and are validated by scientific consensus can be made available to the ecosystem.

Blockchain technology is increasingly gaining adoption in several environments. Aviation (Ahmad et al., 2021), robotics (Lopes and Alexandre, 2019; Strobel et al., 2020), agriculture (Xiong et al., 2020), healthcare (Saeed et al., 2022; Thakur, 2022) and supply chain management (Munir et al., 2022) are examples of activities where this technology is explored. The idea of blockchain applicability in the field of anesthesiology has been proposed (Bellini et al., 2019; Jain, 2019). While specific implementations to use blockchain for the anesthesia records have been suggested (Figar Gutiérrez et al., 2021), our technology concept differs in two fundamental aspects: a) the proposed architecture seeks to not depend on monolithic private services and instead, use them if needed, as replaceable modules; and b) we propose to rely on layer-one blockchains to take technological advantage of the security and decentralization of their consensus mechanisms. That is, implement “real blockchains” and “real politically decentralized storage systems” and not depend on “storage services with blockchain characteristics”.

Conceptually, we seek to develop an integral architecture that minimizes systemic over encapsulated complexity (Buterin, 2022c) while preserving decentralized governance, data immutability and accessibility. On this basis, fully decentralized computing networks (as proposed by Flux ¹⁹ and Internet Computer ²⁰ ) should be conceived as a plausible implementation to allocate the computing power infrastructure of the system. Also in this context, decentralized wireless networks (as proposed by Helium ²¹ ) should be considered as an ideal global communication technology for the devised architecture.

The work of anesthesiologists within health systems is exceptional since they objectively measure multiple parameters related to patients, machines, medications, and diverse technological resources. The proposed system provides continuous data streams whose inherent characteristics make them suitable for feeding analytics systems and for supplying off-chain data to blockchain oracles (Caldarelli, 2020; Ezzat et al., 2022). The generated data is technically valuable as it originates from a complex control system in which many variables are measured and whose different components have distinctive reasons to support the quality of the registration process. These components are naturally coordinated to generate a robust and coherent data flow. The concept should be explained from four different perspectives, each of which presents specific incentives for preserving data integrity and for preventing other parties from colluding against the system:

Patients: Patients logically demand safe anesthesia and quality of care. They expect their anesthesiologist to be qualified and trained to resolve any potential complication. Patients are aligned with the preservation of data integrity, since it prevails as evidence in cases of harm or death. Records are also valuable as documentation of their anesthesiology history for future interventions.

Anesthesiologists: Although practitioners are naturally motivated to provide safe anesthesia to their patients, they are also expected to benefit from data integrity and legitimacy as it feeds their professional log and builds their reputation in the system. In addition, the recorded evidence of compliance with safety standards is a technological protective factor against liability risks.

Society: Data integrity contributes to society by improving anesthesiology outcomes and quality through scientific research. Data tampering is expected to be repelled and reported, as adulterated data can undermine patient safety.

Companies: Companies are expected to appreciate transparent data as they can leverage metrics, supply chain analytics and pharmacovigilance. Rigorous data drives free and fair competition and provides a major incentive for companies to oppose data fraud.

The described system components interact so that each party is encouraged to take advantage of legitimate data and motivated to verify the reliability and validity of the other components. This mechanism of cross auditable interactions provides a collusion resistant system optimized to provide accurate data flows.

Human life can be considered the worthiest asset. Estimating its intrinsic value is analytically complex and objectively unfeasible due to the myriad of variables that should be taken into consideration. However, anesthesiology provides scenarios in which human life is in a standardized state of potential vulnerability due to the lack of self control. Here, the costs and performance associated with the safe care of human life can be objectively measured.

The value of human “health” is also difficult to calculate due to the subjectivity of considering the specific characteristics of patients and of measuring isolated variables from health systems. For example, it would be impractical to establish a standard health value based on a surgeon’s skills, or on a tangible item such as a specific medication or treatment. Both approaches would result in deviations due to interindividual skill disparities in the former, and medication price volatility over time in the latter.

We propose that real-time multivariable recordings can be used to objectively determine the costs related to intraoperative anesthesia procedures through the integration of biosensor time series (and hence data on the performance of the anesthesiologist) with information related to case complexity, medical resource requirements, pharmacological consumption and use of technology. Global statistics can reduce variability and eliminate bias and inconsistencies.

A decentralized blockchain oracle can rule an open-access standard price in health systems which can be used as a financial reference value. This can provide a mechanism to scientifically determine minimum ethical wages for anesthesiologists (and extensively for other health professionals). The latter should be understood as the minimum payment rate that professionals should ethically receive during a specific procedure for health systems to coherently guarantee compliance with safety standards. This can prevent detrimental consequences from unfair or distorted billing systems which, through professional overload (West et al., 2018; Afonso et al., 2021; De Hert, 2021), can lead to decreased situational awareness (Schulz et al., 2017), sleep deprivation (Howard et al., 2003; Neuschwander et al., 2017; Wong et al., 2018) or indirectly induce unsafe practices such as simultaneous anesthesia (Burns et al., 2022), absence from the operating room, or protocol circumvention.

Oracles can automatically classify anesthesiological procedures based on analytical criteria such as patient characteristics, case complexity, procedure duration, incidence of complications, professional proficiency and training requirements. An updated categorization of anesthesiological procedures can improve patient safety by establishing a scientific scale for professional fees, so that cases that are more sophisticated or require more complex skills, are higher ranked. We believe that anesthesia billing structures should position the economic incentives to reward continuous professional education, training and deliberate practice (Hastings and Rickard, 2015), overcoming schemes that can potentially lead to academic outdating or competency obsolescence.

An “anesthesiology blockchain oracle” fed with continuous multivariable data streams can theoretically engender a token tied to healthcare. Global intraoperative sampling could provide a formal way to estimate the value of assuming the responsibility for taking care of human life.

Interestingly, the performance evaluation of intraoperative anesthesia through the proposed measurement system may contribute to practical solutions for some crypto-economic problems termed as “Proof of Excellence” and “Decentralized Success Metrics” (Buterin, 2019). The former refers to token distribution and involves creating a task that only humans can do (or are the only ones authorized to do) but that computers can easily verify. The latter problem consists in implementing a decentralized method for measuring numerical real-world variables.

Finally, it should be noted that an anesthesiology oracle could provide a useful source of entropy (such as League of Entropy ²² and Random.org ²³ ), operating as a publicly verifiable randomness beacon (Stipčević and Koç, 2014).

The interrelation between blockchain technology and distributed storage should provide anesthesiologists with a universal professional record. A personal logbook can be made globally available supplying interoperable evidence of professional proficiency and expertise. Quantities, types and performances of cases carried out during an anesthesiologist´s professional career could be verified on the decentralized and immutable register.

Wearable transponders enable professionals in training stages to formally register their actively supervised cases. Similarly, those in charge of academic training are able to accredit their activity as supervisors.

The envisaged system should contribute to the anesthesiologist community by providing a transparent tool to issue evidence of professional suitability and thus facilitate transnational interoperability. Work experience involving geographical exchanges should enhance the spread of knowledge and professional development.

The system aims to foster individuals to seek excellence in their generated data. Records can be categorized according to the quality of their measurements and their completeness. Targeted incentives can be granted for provenance-rich data, uninterrupted sampling, interference-free datastreams, continuous proximity to the supervised patient and for the quality of the supplementary information. Users can be referenced according to the suitability of their records for use in scientific research. Greater demand is expected for high-quality data generated by better referenced users.

User profiles linked to unique blockchain anti-Sybil identities provide the means for a voting system, enabling affiliates to reach consensus and take decisions collectively without relying on a central authority (Honkanen et al., 2021). Decisions on the direction of the ecosystem, such as improvement proposals and technology implementations can be achieved through the decentralized polling mechanism (Zwitter and Hazenberg, 2020).

A scheme of challenges can incentivize the network to verify the professional performance, accreditation fulfillment, and safety compliance of its participants, thus ensuring the scientific and ethical integrity of the system. In this way, the admission and rejection of users is constantly determined by the consensus of the ecosystem. Scalability and global adoption can drive a “network state” (Srinivasan, 2022): a translational online community of anesthesiologists organized around the values of Science (Bunge, 2018).

The costs of system maintenance, processing power, data storage, audit incentives and blockchain fees must be covered by a sustainable contribution mechanism. While a payment system analysis is beyond the scope of this paper, potential implementations of blockchain technology should be considered.

We believe that from an ethical perspective the system’s cost should be covered by users themselves and patients should not be charged. A real-time blockchain micropayment system (Klein and Stummer, 2021) applied during the data transmission process can provide a fair alternative to the subscription model, such that users are charged only by their transmitted data and can freely decide whether to record their specific cases on the system.

Taking advantage of blockchain transparency and traceability, a cryptocurrency treasury can be established and controlled by the governance layer through the voting system. Smart contracts can be programmed to automatically pay the costs for storing and pinning services directly from the system reserves. The treasury can provide funds according to the ecosystem´s investment interests such as scientific research, production of educational content, simulation training, technology development and provision of resources for anesthesiologists in areas of need (Warner et al., 2022). Also, in return for generating the anesthesia record, patients and anesthesiologists can be insured by the system treasury (Shetty et al., 2022). In this way, costs of potential legal demands and compensation for injuries or sequelae during recorded anesthesia procedures can be covered directly by the system.

Users can be randomly requested to audit anonymous records in order to interpret cases which need clarification. Worldwide users with diverse professional backgrounds can determine through recorded measurements whether the execution of the audited anesthesia was carried out competently in accordance with safety protocols. Through this mechanism, the ecosystem consensus can resolve the release of funds for each particular case in a fair and impartial way.

Part of the system liquidity can be collectively managed to generate income through the investment in projects or protocols enabling users to reduce (and potentially eliminate) their system usage expenses. Not only this could result in a feasible cost-free service and professional insurance, but also in a potential retirement fund system (Sarker and Datta, 2022). Furthermore, utility tokens can be minted by the system mechanics contributing to its sustainability through the application of strategic tokenomics (Freni et al., 2022).

It is expected that any anesthesia record may require an audit or an expert opinion emitted by a third party or legal entity. The system can contribute by providing a mechanism for cooperative issuance of formal interpretations.

Users with different backgrounds and diverse professional expertise can be incentivized to review records and submit their findings and conclusions. Sustainability of the audit structure can be achieved by paying contributors according to professional expertise.

In order to guarantee confidentiality and impartiality throughout the entire review process, traceable content is hidden and records are kept anonymous. Reports on records are authenticated by their reviewers and made openly available enabling the ecosystem to audit and object to the review process. Users can be randomly incentivized to create and inject fake records with incorrect, imprecise or unreliable information in order to reward reviewers who consistently detect inaccuracies.

In addition to auditing purposes, a report system should be available to communicate accidents, near misses and rare events related to patients, medications, surgical procedures, or equipment performance. In this way colleagues can evaluate, provide suggestions and validate data for its correct interpretation. A library of scientific case reports selected by the ecosystem can be created for educational purposes.

Feedback control systems have potential application in the field of anesthesiology. Artificial intelligence algorithms can integrate datastreams in real time and efficiently anticipate the physiological and biochemical evolution of patients (Hashimoto et al., 2020).

Upcoming technology will likely provide automated control of drug infusion systems and anesthesia machine parameters, leading to more precise and accurate performance (Zaouter et al., 2020). Although from a safety perspective automated systems are not expected to replace human anesthesiologists, new technologies will probably expand their tasks and require professionals to operate and supervise automatic control systems (Lonsdale et al., 2020). This will propel professional development of future anesthesiologists into formal science fields such as mathematics, computing and engineering.

The development of control systems driven by artificial intelligence will require implementation stages in real scenarios (Magrabi et al., 2019; Canales et al., 2020). The immutable, authenticated and timestamped data generated by our proposed architecture is theoretically applicable for optimally feeding these systems and for providing continuous technological surveillance of innovative implementations (Parvinian et al., 2018). We believe that systems which exert control over humans through artificial intelligence should be auditable through an open science framework (Paton and Kobayashi, 2019).

Intraoperative anesthesia provides exceptional opportunities for the sampling of EEG data. Diverse cases require different types of anesthesia; from sedation to deep hypothermic circulatory arrest (Mavroudis et al., 2018; Alkhatip et al., 2021). Open source neurotechnology and standardized protocols can provide vast datasets for neuroscience research.

We believe that anesthesiology should play a leading role in addressing fundamental problems of neuroscience. There is arguably no medical specialty better positioned to pursue a comprehensive explanatory framework for understanding how the electrochemical activity of the brain generates perception; also known as “the hard problem of consciousness” (Chalmers, 1995). Assessment of the activity and interactivity of neural structures during different states of anesthesia can elucidate the core requirements for conscious experience.

Coordinating efforts through a collective “laboratory” composed of operating rooms around the world measuring information processes about conscious state transitions is expected to generate valuable data to consolidate physiological knowledge of neural encoding (Mashour, 2022). Making foundational scientific contributions to understanding the neural basis for consciousness is important for anesthesiology since solving this challenge will provide insights into what brains are naturally engineered to do (Alkire et al., 2008). Ultimately, this knowledge will help in understanding how to improve patient safety.

Simulation-based education in anesthesiology has gained wide integration over time (Higham and Baxendale, 2017). However, development of anesthesia software simulators has been limited and scarce evidence of their implementation is available in the current literature. We believe that the potential of software simulation is under-exploited. Unfriendly operability, outdated graphics, limited scenarios and expensive subscriptions for individuals can negatively affect their widespread adoption (Edwards and Lampotang, 2020).

The system treasury can fund and sustain a continuously updated high fidelity software simulator.

Next-generation game engines (such as Unreal Engine 5 ²⁴ ) can deliver photorealistic point-of-view simulation environments (El-Wajeh et al., 2022). Furthermore, virtual and augmented reality technologies may be adopted over time if their generated immersive experiences prove to be superior in cost/effectiveness terms over screen-based simulation (Alam and Matava, 2022).

To provide evidence-based scenarios, simulations can be fed by global data generated by real cases. Intraoperative physiology and biochemistry can be simulated by integrating big data from anesthesia records enhanced by machine learning technology.

Anesthesiologists can learn and train with a high-fidelity software simulation platform and test their proficiency through automatic system examinations which generate blockchain certifications. Pushing this idea further, official examinations can be enhanced by compensating users with system retributions (such as credits or storage capacity), for the simulated time according to payment rates consistent with actual anesthesia cases, therefore mitigating the obstacle of prolonged times required to perform truthful scenarios. This would reduce the bias of “short latency times” generating a realistic length simulation in which subjects do not tend to become hypervigilant while aware of being in a simulation, expecting critical events to be triggered imminently in the first minutes.

The wearable transponder can be used to validate users performing simulations by matching their acceleration patterns with movements in the virtual environment.

A decentralized educational platform can be created by funding high quality multimedia content creators through community voting (Leible et al., 2019; Rahardja et al., 2021). Educational content may include lectures, tutorials, anesthesiological metrics, case reports, measurements on specific procedures, near-misses and accident reconstructions.

System governance can democratically fund research and scientific publishing in accordance with the ecosystem academic objectives (Mackey et al., 2019).

Although this proof-of-concept advocates decentralization, it is important to note that a governance layer is necessary in order to ensure scientific rigor. This implies that a self-governed community should be able to determine which professionals are admitted or rejected, based on accreditation of professional certifications, compliance with patient safety and adherence to quality standards for data generation. It should be noted that decentralized governance may introduce potential drawbacks to the system that will require its definitive implementation to be a meticulous process (Buterin, 2021).

A decentralized autonomous organization (DAO) can potentially be assembled on top of the system (Ziolkowski et al., 2020; Liu et al., 2021); however, since there is no precedent for similar implementations in anesthesiology, we believe that full decentralization should be achieved gradually if possible (Buterin, 2022d; Henrik et al., 2022).

Whereas different blockchains may be implemented (Tasca and Tessone, 2019), it would be opportune to materialize their functionalities through projects founded on peer-reviewed research (such as Cardano) ²⁵ and developed through evidence-based methods consistent with our system’s inherent philosophy.

We anticipate adoption resistance as a primary limitation to scalability. Unfamiliarity about novel technologies and the need to use additional hardware may restrain acceptance by professionals (Lustenberger et al., 2021). However, we foresee that as the global community gradually assimilate the benefits of decentralization and data immutability, the proposed system architecture may become a new paradigm in the way data is recorded in operating rooms (Pandya et al., 2021). In the long run, global adoption is expected to be driven by patient demand for modern safety technologies.

Keeping control of patients’ parameters in all scenarios is a central goal of anesthesiologists. We believe that health professionals who work with control systems should stream and record immutable data as proof of transparency and respect for human life. Great progress in the field can be driven by generating accurate records and making measurements openly available to science.

The proposed architecture seeks to provide a transformative path for anesthesiology, assuming as a guiding principle that “better information makes for better decisions”. In summary, this is how we believe technology should be implemented to scientifically improve patient safety, outcomes, and quality.