Instant Patient State Assessment in Emergency and Intensive Care: Clinico-Physiological Rationale for a Digital Model

Authors

1. Prozorov A.A., Director of Technology, RTLAB; Architect, Sbertech; ap@rtlab.ru;
2. Redkin I.V., Director of Science, RTLAB; Cand.Sc. (Med.); Leading Research Scientist, Negovsky Research Institute of General Reanimatology, FSCC for Resuscitation and Emergency Medicine; iredkin@rtlab.ru;

Abstract

Objective. To formulate the medical content of instant patient state assessment in emergency care, anesthesiology-critical care medicine, and intensive therapy; to define its physiological foundations, clinical boundaries, and requirements for the use of continuous signals, derived features, and codified clinical terms.

Materials and Methods. A conceptual literature search and critical analysis of source documents on the HealthOS platform was performed, with terminological alignment to contemporary language in anesthesiology-critical care medicine, intensive therapy, physiological monitoring, and medical device software. External anchors included WHO/WHA documents on integrated emergency, critical and operative care, guidance on early recognition of clinical deterioration, Sepsis-3, SOFA, Surviving Sepsis Campaign 2026, ESICM documents on shock and hemodynamic monitoring, and publications on homeostasis, allostasis, physiological complexity, capnography, alarm signals, and clinical decision support software.

Results. Instant patient state assessment is defined as a reproducible logic-clinical inference of the current physiological status from multichannel streaming data and codified medical terms. It is argued that the clinical meaning of such a status is formed not by a single parameter, but by a coherent interpretation of cardiovascular, respiratory, neurovegetative, renal, metabolic, and therapy-conditioned dynamics. Key features of the model are compensation, physiological reserve, support-dependent stability, a decompensation trajectory, inter-channel coherence, and verifiability of evidence.

Conclusion. Instant patient state assessment must not be treated as an autonomous diagnosis, triage category, prognostic score, monitor alarm, or automatic treatment order. Its correct clinical use is possible only as an evidence layer supporting physician decision-making that preserves data traceability, explicit signal-quality assessment, boundaries of algorithmic interpretation, and the need for prospective clinical validation.

Keywords: instant patient state assessment; emergency care; intensive therapy; anesthesiology-critical care medicine; physiological monitoring; clinical deterioration; compensation; decompensation; physiological reserve; capnography; vasopressor support; SaMD; clinical decision support software

Abbreviations and Terminological Designations

BP — blood pressure; MAP — mean arterial pressure; vasopressor support — ongoing pharmacologic support to preserve blood pressure and perfusion under instability; WHO — World Health Organization; ECO — integrated emergency, critical and operative care; MV — mechanical ventilation; ICU — intensive care unit.
EtCO2/PetCO2 — end-tidal carbon dioxide concentration or partial pressure; FiO2 — inspiratory oxygen fraction; MEWS — Modified Early Warning Score; NEWS2 — National Early Warning Score 2; SaMD — Software as a Medical Device; SOFA — Sequential Organ Failure Assessment; SpO2 — peripheral oxygen saturation.
The terms “codified term,” “named term,” and “codified clinical term” are interchangeable in this article and refer to a single medical object.

1. Introduction

Emergency medicine, anesthesiology-critical care medicine, and intensive therapy share a common task: to recognize in time the moment when a patient is already clinically unstable, maintains vital functions only through strenuous compensation, or is moving toward systemic decompensation. In this context, “state assessment” is not a simple enumeration of monitor numbers. It requires clinical interpretation of interrelated physiological processes, the rate of their change, and the patient’s dependence on ongoing support.

The modern concept of integrated emergency, critical and operative care emphasizes that emergency, critical, and operative care form a single continuous system of medical safety, and that delay in recognizing deterioration can lead to preventable mortality and disability [1,2]. In clinical practice, early warning scores, rapid-response protocols, organ-dysfunction criteria, and monitoring standards are used for this purpose [3-6]. However, these tools address limited tasks: they help structure observation and response but do not exhaust the medical meaning of the current physiological status.

Monitoring density in the operating room, ICU, emergency department, high-risk ward, and during in-hospital transport is constantly increasing. The clinician simultaneously evaluates the ECG, arterial waveform, plethysmogram, SpO2, EtCO2, ventilator parameters, urine output, lactate, blood gas composition, level of consciousness, vasopressor doses, depth of sedation, perfusion signs, and procedural context. Therefore, the medical object to be described is broader than a “monitor alarm,” a “score,” or a “provisional diagnosis.”

The aim of this article is to present a revised scientific version of the concept of instant patient state assessment as an independent clinico-physiological object and to define requirements for its representation in digital monitoring and clinical decision support systems.

2. Methodological Framework

This work is a conceptual article and a scientific review of the source text. It is not a systematic review, meta-analysis, clinical effectiveness study, or regulatory dossier for a software product. A narrative synthesis was used across highly cited sources and current clinical documents from emergency medicine, intensive therapy, anesthesiology, physiological monitoring, sepsis, shock, capnography, clinical deterioration, software as a medical device, and clinical decision support software.

Sources were grouped into five directions: organization of emergency and critical care; recognition of deterioration and early warning scores; sepsis, shock, and organ dysfunction; biology of homeostasis, allostasis, and physiological reserve; digital monitoring and clinical decision support software. This approach matches the article’s aim: not to prove the effectiveness of a specific algorithm, but to clarify the medical content of an object that must be formalized before development and clinical testing of a digital system.

3. Definition and Clinical Boundaries

Instant patient state assessment is a logically rigorous and reproducible inference of a clinically meaningful current physiological status from continuous oscillograms, streaming physiological signals, derived features, and codified clinical terms in real time.

Five positions are essential in this definition. First, the object is not a nosological diagnosis but the current functional state of the organism. Second, the state is described as an interaction of systems, not as a set of independent abnormalities. Third, compensation and reserve are part of clinical meaning, not secondary comments. Fourth, decompensation is treated as a trajectory of lost stability, not only as the moment a threshold is crossed. Fifth, therapeutic context is included in interpretation: normal MAP on a rising norepinephrine dose and normal SpO2 at high FiO2 are not equivalent to restored reserve.

Therefore, instant patient state assessment is not equivalent to diagnosis, triage category, severity score, prognostic model, monitor alarm signal, recommendation, or treatment order. It may support clinical attention, escalation of observation, diagnostic refinement, or therapy revision, but must not replace physician decision-making.

Table 1. Subject boundaries of instant patient state assessment

Dimension	What it is	What it is not
Primary object	Current physiological status of the patient	Nosological diagnosis by itself
Time horizon	Real time and nearest short-term dynamics	Retrospective description only
Logic of interpretation	State of interrelated physiological systems	A list of isolated parameters
Clinical meaning	Compensation, reserve, support, trajectory, and risk of decompensation	A single NEWS2, MEWS, or SOFA score
Link to action	Basis for physician re-evaluation and clinical decision	Automatic command or ready-made treatment order

4. Biological Basis: Homeostasis, Allostasis, and Reserve

Physiological health during acute illness does not mean that every individual parameter is normal. The organism may maintain blood pressure, gas exchange, or minute ventilation at the cost of marked sympathetic activation, vasoconstriction, increased work of breathing, altered vascular tone, redistribution of blood flow, fluid retention, and metabolic mobilization. Such compensation may be effective but not always safe: under prolonged or excessive load it becomes a factor of injury and organ dysfunction [12-14].

The concepts of homeostasis and allostasis distinguish static normality of numbers from dynamic stability of regulation. Homeostasis describes maintenance of the internal environment within acceptable limits, whereas allostasis emphasizes adaptive change in regulatory parameters in response to load. In critical illness this adaptation can become costly: visible stability may be maintained only by shrinking physiological reserve.

Physiological reserve should be understood as the patient’s ability to tolerate additional load without loss of coherent function. Its reduction may appear not only as hypotension or hypoxemia, but also as decreased variability, monotonic trends, repeated instability episodes, rising support requirements, and impaired inter-system coherence [15,16]. For a digital model this implies the need to assess not only absolute values, but also rate of change, stability, repeatability, recovery, and response to intervention.

5. Organ Dysfunction, Shock, and Systemic Decompensation

Sepsis and shock are clinical anchors that demonstrate the limits of single-factor monitoring. Sepsis-3 defines sepsis as life-threatening organ dysfunction caused by dysregulated host response to infection [7]. SOFA is a tool for describing organ dysfunction, but by itself does not exhaust the dynamic meaning of the current status [8]. Updated Surviving Sepsis Campaign 2026 recommendations emphasize clinically contextual diagnosis, timely antibacterial therapy, perfusion assessment, and repeated patient re-evaluation [9].

Shock also cannot be reduced to MAP. The ESICM consensus and updated hemodynamic monitoring recommendations treat shock as a complex state in which perfusion, cardiac output, response to fluid loading, echocardiography, tissue hypoperfusion markers, and clinical signs matter [10,11]. Hemodynamic coherence — consistency among macrocirculation, microcirculation, and tissue oxygenation — may be impaired; achieving target MAP does not always mean restored microvascular perfusion [17,18].

Consequently, instant state assessment should record not only the presence of an abnormality, but also its systemic context: whether the patient is stably compensated, stable only on intensive support, whether inter-channel coherence is worsening, and whether a trajectory toward decompensation is forming.

6. Observable Layers and Clinical Interpretation

Data entering an instant assessment system are usefully divided into three layers. The first layer is continuous signals and oscillograms: ECG, arterial waveform, plethysmogram, capnography, respiratory curves, ventilator parameters, and SpO2. The second layer is derived features: trends, variability, slope, repeatability, episode duration, arrhythmic burden, pulse pressure width, EtCO2 stability, desaturation episodes, and asynchrony with the ventilator. The third layer is codified clinical terms: vasopressor dose, FiO2, PEEP, ventilation mode, lactate, blood gas composition, urine output, level of consciousness, depth of sedation, blood loss, transfusion, transport, or intervention.

Clinically, no layer is self-sufficient. Raw signals require quality assessment, artifact filtering, and comparison with other channels. Derived features require a valid observation window and correct temporal binding. Codified terms must have a source, currency, and validity period. Only joint interpretation of these layers allows distinction of true recovery from support-dependent stability and early decompensation.

Table 2. Observable layers and clinical meaning

System/context	Signal layer	Derived layer	Codified layer	Clinical interpretation
Cardiovascular system	ECG, arterial waveform, plethysmogram	HR variability, arrhythmic burden, pulse pressure width	lactate, vasopressors, transfusion, blood loss	perfusion, shock risk, support-dependent stability
Respiration and gas exchange	SpO2, capnography, respiratory curves, ventilator parameters	EtCO2 trend, desaturation episodes, asynchrony	FiO2, PEEP, MV mode, blood gases	oxygenation, ventilation, respiratory reserve
Nervous and autonomic regulation	HR dynamics, signal reactivity	reduced variability, autonomic strain	consciousness, sedation, delirium, seizures	central regulation and risk of occult deterioration
Kidneys and fluid balance	pressure and perfusion context	reduced perfusion reserve	urine output, creatinine, fluid balance	AKI risk, overload, or volume deficit
Therapeutic context	device-dependent curves and parameters	inability to normalize without intensified support	drug doses, infusions, procedure, transport	distinction of intrinsic vs supported stability

7. Compensation and Support-Dependent Stability

Compensation in emergency medicine means active preservation of function against injury, stress, or reduced reserve. It may manifest as tachycardia, vasoconstriction, hyperventilation, increased work of breathing, fluid retention, substrate mobilization, and neuroendocrine activation. Compensation is not recovery: its cost may rise while reserve falls.

Support-dependent stability is an intermediate and clinically hazardous state. The patient may have acceptable MAP, SpO2, or pH only with rising vasopressor support, high FiO2 and PEEP, frequent ventilator correction, or massive infusion therapy. Here the key question is not “is the number normal,” but “does therapy restore reserve or temporarily hold the system back from decompensation.”

Decompensation should be understood as a process of reserve loss. It may begin before overt collapse: recurrent ectopy, narrowing pulse pressure, rising lactate, falling urine output, worsening capnography, ventilator asynchrony, increasing vasopressor load, or reduced variability. Instant assessment should be sensitive precisely to this trajectory.

8. Short-Term Dynamics and Time Windows

In intensive therapy, time changes the meaning of a sign. A single deviation may be an artifact or brief reaction, whereas repeatability and persistence create a clinical pattern. At the second level, sudden arrhythmia, loss of pulse contour, sharp EtCO2 change, or desaturation are assessed. In a 1-3 minute horizon, episode stability is determined. In a 5-10 minute interval, trends, recovery, inter-channel coherence, and response to support gain importance. In a 10-15 minute horizon, pattern assessment becomes possible: is reserve recovering or is a coherent decompensation trajectory forming?

These intervals are clinically illustrative, not normative. Their purpose is to show that “instant” assessment does not mean a single-point value. The subject is a real-time status in which the nearest seconds, minutes, and short-term dynamics matter.

9. Requirements for Digital Implementation

A system using instant patient state assessment must preserve the distinction among data, interpretation, and clinical decision. If a software module provides structured information to the physician, it must show the data source, channel quality, time window, rules for forming derived features, currency of codified terms, and boundaries of interpretation. This is especially important for clinical decision support software and Software as a Medical Device, where regulatory significance lies in whether the specialist can independently evaluate the basis of the output [22,23].

Practical requirements include: signal quality control; explicit artifact marking; temporal synchronization of channels; storage of therapeutic context; traceability of transformations; reproducibility of rules; algorithm version logging; prohibition of turning an indicator into an autonomous order; physician review capability; and subsequent clinical validation on prospective data.

Table 3. Requirements for a digital instant assessment system

Requirement	Clinical goal	Risk if absent
Channel quality control	Exclusion of artifactual and unreliable signals	False alarm or missed deterioration
Temporal synchronization	Correct comparison of signals, laboratory data, and therapy	Erroneous causal-temporal linking of events
Feature traceability	Physician and technical audit capability	Non-reproducible output
Therapy accounting	Distinction of recovery vs support-dependent stability	Underestimation of severity
Limitation of automation	Preservation of physician role in diagnosis and treatment	Clinical over-assertion of algorithm output

10. Discussion

The proposed terminological framework is useful for uniting medical and cybernetic logic. Medically, it keeps focus on the patient’s physiological state rather than isolated numbers. From an engineering standpoint, it requires formal description of data, time, quality, evidence, and boundaries of algorithmic inference. This union is especially important for continuous monitoring systems, where a data stream may create an illusion of objectivity yet, without clinical semantics and quality control, can amplify erroneous conclusions.

The main risk is clinical over-assertion. A deterioration indicator derived from streaming signals and terms must not become a diagnosis of sepsis, shock, respiratory failure, or an indication for specific therapy without physician analysis. A second risk is hidden dependence on data quality: ECG artifact, incorrect pressure calibration, poor capnography line, or incomplete therapy recording can create a false pattern. A third risk is ignoring therapy: stability maintained by high-intensity intervention is not equivalent to intrinsic physiological resilience.

11. Limitations

This article is not clinical validation of a software system, does not prove diagnostic accuracy of an algorithm, and does not propose a therapeutic protocol. The model presented describes a medical object and requirements for its formalization. Transition to clinical use requires data-collection protocols, independent labeling, prospective studies, assessment of impact on physician decisions, safety, regulatory classification, and development quality control.

12. Conclusion

Instant patient state assessment in emergency and intensive care should describe not a set of isolated abnormalities, but a regulated systemic state of the organism. Its clinical value is determined by the ability to account for compensation, reserve, dependence on support, short-term dynamics, data quality, and inter-channel coherence. In a digital system, such an inference must remain evidence-based, traceable, and available for physician review, without becoming an autonomous diagnosis or automatic treatment order.

Conflict of Interest

The manuscript describes implementation of a specific aspect of the HealthOS platform, namely: the clinico-physiological rationale for a model of instant patient state assessment in emergency care, anesthesiology-critical care medicine, and intensive therapy.

Funding

Work funded by RTLAB.

References

1. World Health Organization. Integrated emergency, critical & operative care (ECO). Geneva: WHO; updated information resources, accessed 2026.
2. World Health Assembly. WHA76.2. Integrated emergency, critical and operative care for universal health coverage and protection from health emergencies. Geneva: WHO; 2023.
3. National Institute for Health and Care Excellence. Acutely ill adults in hospital: recognising and responding to deterioration. Clinical guideline CG50. London: NICE; 2007.
4. Royal College of Physicians. National Early Warning Score (NEWS) 2: standardising the assessment of acute-illness severity in the NHS. Updated report of a working party. London: RCP; 2017.
5. Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified Early Warning Score in medical admissions. QJM. 2001;94(10):521-526.
6. Mitchell OJL, McKay H, Van Leuvan C, et al. Defining physiological decompensation: an expert consensus and retrospective outcome validation. Crit Care Explor. 2022;4(4):e0677. doi:10.1097/CCE.0000000000000677.
7. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801-810. doi:10.1001/jama.2016.0287.
8. Vincent JL, Moreno R, Takala J, et al. The SOFA score to describe organ dysfunction/failure. Intensive Care Med. 1996;22:707-710. doi:10.1007/BF01709751.
9. Prescott HC, Antonelli M, Alhazzani W, et al. Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock 2026. Intensive Care Med. 2026. doi:10.1007/s00134-026-08361-1.
10. Cecconi M, De Backer D, Antonelli M, et al. Consensus on circulatory shock and hemodynamic monitoring. Intensive Care Med. 2014;40(12):1795-1815. doi:10.1007/s00134-014-3525-z.
11. Monnet X, Messina A, Greco M, et al. ESICM guidelines on circulatory shock and hemodynamic monitoring 2025. Intensive Care Med. 2025;51:1971-2012. doi:10.1007/s00134-025-08137-z.
12. Cuesta JM, Singer M. The stress response and critical illness: a review. Crit Care Med. 2012;40(12):3283-3289. doi:10.1097/CCM.0b013e31826567eb.
13. Kotas ME, Medzhitov R. Homeostasis, inflammation, and disease susceptibility. Cell. 2015;160(5):816-827. doi:10.1016/j.cell.2015.02.010.
14. Ramsay DS, Woods SC. Clarifying the roles of homeostasis and allostasis in physiological regulation. Psychol Rev. 2014;121(2):225-247. doi:10.1037/a0035942.
15. Goldberger AL, Peng CK, Lipsitz LA. What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging. 2002;23(1):23-26. doi:10.1016/S0197-4580(01)00266-4.
16. Goldberger AL, Amaral LAN, Hausdorff JM, Ivanov PCh, Peng CK, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA. 2002;99(Suppl 1):2466-2472. doi:10.1073/pnas.012579499.
17. Arnemann P, Seidel L, Ertmer C. Haemodynamic coherence - the relevance of fluid therapy. Best Pract Res Clin Anaesthesiol. 2016;30(4):419-427. doi:10.1016/j.bpa.2016.11.003.
18. Bakker J, Ince C. Monitoring coherence between the macro and microcirculation in septic shock. Curr Opin Crit Care. 2020;26(3):267-272. doi:10.1097/MCC.0000000000000729.
19. Taenzer AH, Spence BC. The afferent limb of rapid response systems: continuous monitoring on general care units. Crit Care Clin. 2018;34(2):189-198. doi:10.1016/j.ccc.2017.12.001.
20. Paine CW, Goel VV, Ely E, et al. Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136-144. doi:10.1002/jhm.2520.
21. Wollner EA, Nourian MM, Bertille KK, Wake PB, Lipnick MS, Whitaker DK. Capnography - an essential monitor, everywhere: a narrative review. Anesth Analg. 2023;137(5):934-942. doi:10.1213/ANE.0000000000006689.
22. International Medical Device Regulators Forum. Software as a Medical Device (SaMD): possible framework for risk categorization and corresponding considerations. IMDRF/SaMD WG/N12FINAL:2014. 2014.
23. U.S. Food and Drug Administration. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff. Silver Spring: FDA; 2026.
24. Kochkin AA, Yavorsky AG, Berikashvili LB, Likhvantsev VV. Modern vasopressor therapy for septic shock (review). General Reanimatology. 2020;16(2):77-93. doi:10.15360/1813-9779-2020-2-77-93.
25. Berikashvili LB, Kuzovlev AN, Yadgarov MYa, et al. Prognostic ability of the MEWS nomogram after cardiac surgery in the ICU. Bulletin of Anesthesiology and Reanimatology. 2022;19(2):6-13. doi:10.21292/2078-5658-2022-19-2-6-13.