Automated Monitoring Reveals Underreporting of Oxygen Consumption in Electronic Medical Records

Automated Monitoring Reveals Underreporting of Oxygen Consumption in Electronic Medical Records | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Automated Monitoring Reveals Underreporting of Oxygen Consumption in Electronic Medical Records Vitor Ulisses Monnaka, Ana Laura Moraes Silva, Denis Faria Moura Junior, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8280073/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Accurate monitoring of oxygen therapy is essential for patient safety, resource management, and financial accountability. However, oxygen consumption is often underreported in manual electronic medical record (EMR) documentation. Automated monitoring technologies, such as ATAS O₂ (Salvus, Pernambuco, Brazil), have emerged as a promising solution to this limitation. This retrospective study compared oxygen consumption data recorded in the EMR with measurements obtained using ATAS O₂ across 21 hospital beds over a five-month period. The ATAS O₂ system detected a higher number of beds receiving oxygen, total consumption time per bed (median 533 vs. 48 h, p < 0.001), frequency of cycles (median 96 vs. 10, p < 0.001), mean of oxygen flow rates (median 2.19 vs. 0.58 L/min, p < 0.001), and total volume consumed per bed (median 82 vs. 1.84 L, p < 0.001). In contrast, EMR documentation captured longer mean duration per cycle (11.17 ± 12.07 vs 6.64 ± 8.20 h, p = 0.082) and higher oxygen volume consumed per cycle (median 269 vs 0.58 L, p < 0.001). These findings indicate systematic underreporting in manual EMR documentation and highlight the potential benefits of automated monitoring of oxygen therapy in improving accuracy, optimizing hospital resource management, and supporting financial oversight. Oxygen inhalation therapy Oxygen consumption monitoring Electronic health records Health resources utilization Automation Figures Figure 1 1. Introduction Oxygen is a fundamental medical resource, essential for sustaining patients in critical conditions such as pneumonia, sepsis, and acute respiratory failure ( 1 ). Its central role in patient outcomes underscores the need for efficient supply and rational use. However, the COVID-19 pandemic exposed the fragility of healthcare infrastructures, as the unprecedented surge in demand led to global shortages of medical oxygen ( 2 ). Despite being a vital and limited resource, oxygen is frequently misused in clinical practice, resulting in substantial financial and environmental burdens. Standard flowmeters, when fully opened beyond their calibrated range, can deliver flow rates of 65–75 L/min, far exceeding therapeutic requirements ( 3 ). In one study, 17 out of 21 patients received oxygen flows in excess of 15 L/min. The authors estimated that if only two patients were exposed to such excess flow, nearly 172,800 L of oxygen would be wasted per day, resulting in roughly 63 million liters of oxygen wasted annually ( 3 ). Effective oxygen stewardship requires precise and continuous monitoring of consumption. However, traditional methods remain limited. Oxygen use is typically estimated from flow values manually entered into electronic medical records (EMR) by healthcare professionals, a time-consuming process prone to transcription errors ( 4 ). High clinical workloads often delay or preclude documentation, compromising the accuracy of consumption estimates. Moreover, manual flowmeters may lose calibration overtime due to wear, contamination, or inadequate maintenance ( 5 ), further reducing measurement precision. Finaly, because EMR-based oxygen consumption data often serve as the basis for billing health insurance providers, underreporting or inaccuracies can result in substantial financial losses, as hospitals may be reimbursed for only a portion of their actual oxygen usage. This highlights the need for more reliable monitoring systems to optimize both resource management and financial accountability. Automated monitoring technologies have emerged as a promising approach to overcome the limitations of manual documentation ( 6 ). Systems such as the ATAS O₂ (Salvus, Pernambuco, Brazil) enable continuous, objective measurement of oxygen flow, reducing reliance on manual recording and enhancing both clinical accuracy and operational efficiency. Despite their potential, few studies have systematically evaluated the performance of oxygen monitoring devices against EMR-based documentation under real-world hospital conditions, an essential step to validate their practical and clinical utility. To address this gap, this retrospective study compared oxygen consumption data recorded by the ATAS O₂ system with those documented manually in the EMR. 2. Methods This retrospective study compared oxygen consumption data from 21 beds in the Coronary Step-Down Unit of Einstein Hospital Israelita, collected between September 2021 and January 2022, using two monitoring strategies: the ATAS O₂ (Salvus, Pernambuco, Brazil) device and the electronic medical record (EMR) entries registered by healthcare professionals. The analysis relied exclusively on administrative datasets generated weekly for internal billing verification, composed solely of anonymized medical record numbers and oxygen-therapy documentation status. Oxygen consumption data per bed were summarized into the following parameters: total consumption time (h), number of recorded cycles, mean hours per cycle (h), mean volume consumed per cycle (L), mean oxygen flow (L/min), and total volume consumed (L). To evaluate temporal patterns, data were aggregated monthly across the five study months. Statistical analyses were conducted in SPSS (version 26.0) by the Institutional Department of Statistics. A significance level of 5% was adopted for all tests. Data normality was assessed using the Shapiro–Wilk test and visual inspection of boxplots, histograms, and quantile–quantile plots. Comparisons between monitoring methods were performed using either the Student’s t-test or the Mann–Whitney U test, as appropriate. 3. Results The ATAS O₂ system recorded significantly higher total oxygen consumption time per bed compared with EMR documentation (median 533.19 vs. 48.20 h, p < 0.001; Table 1 ). Surprisingly, ATAS O₂ detected a greater number of beds using oxygen than was documented in EMR (Fig. 1 , left panel), indicating that oxygen use in some beds was not captured manually. This underreporting of beds in EMR partially explains the larger number of cycles recorded by ATAS O₂ (Fig. 1 , middle panel). Nevertheless, even when normalized per bed, ATAS O₂ still showed a consistently greater frequency of cycles compared with EMR (median 96 vs. 10, p < 0.001; Table 1 ), suggesting that the automated system captured more frequent changes in the oxygen flow. Table 1 Comparison of oxygen consumption metrics per bed recorded through Electronic Medical Records (EMR) and the ATAS O₂ device Variables EMR ATAS O₂ P value Total consumption time (h) 48.20 (20.80, 345.20) 533.19 (277.91, 778.99) < 0.001* Number of recorded cycles 10.00 (5.00, 22.00) 96.00 (62.00, 207.00) < 0.001* Mean hours per cycle (h) 11.17 ± 12.07 6.64 ± 8.20 0.082† Mean volume consumed per cycle (L) 269.33 (90.00, 584.75) 0.58 (0.36, 1.21) < 0.001* Mean oxygen flow (L/min) 0.58 (0.35, 0.86) 2.19 (1.46, 2.66) < 0.001* Total volume consumed (L) 1.84 (0.94, 7.81) 82.41 (41.68, 111.74) < 0.001* Values are presented as mean ± SD or median (Q1, Q3). *Mann-Whitney U test, †Student's t-test. In line with these observations, the mean duration per cycle was longer in EMR (Fig. 1 , right panel), starting above 30 hours in September and progressively decreasing throughout the study period, whereas ATAS O₂ reported consistently shorter and more stable cycle durations (approximately 5 hours per cycle). Across the study period, the mean hours per cycle were also lower with ATAS O₂ (6.64 ± 8.20 h) compared with EMR (11.17 ± 12.07 h), although this difference did not reach statistical significance ( p = 0.082; Table 1 ). Consistent with the prolonged cycle times, EMR documentation showed a higher oxygen volume consumed per cycle (median 269.33 vs 0.58 L, p < 0.001; Table 1 ). However, ATAS O₂ detected significantly higher mean oxygen flow rates compared with EMR (median 2.19 vs. 0.58 L/min, p < 0.001; Table 1 ), suggesting that manual documentation underestimates flow intensities during oxygen therapy. In agreement with this finding, the total volume consumed per bed recorded by ATAS O₂ was markedly higher compared with EMR (median 82.41 vs. 1.84 L, p < 0.001; Table 1 ). 4. Discussion In this study, oxygen consumption was consistently underreported in manual electronic medical record (EMR) documentation compared with automated monitoring by the ATAS O₂ system. The ATAS O₂ not only captured longer total durations of oxygen use but also identified a greater number of beds receiving oxygen therapy. Since both systems monitored the same 21 beds, this discrepancy indicates that oxygen use in some patients was not documented in the EMR at all. Manual underreporting also led to longer recorded cycle durations, lower flow rates, and a substantially lower total volume of oxygen consumption per bed compared with ATAS O₂ data. These findings align with previous reports showing significant gaps in the documentation of oxygen prescriptions and patient-reported oxygen use, where only 45% of prescribed oxygen values were accurately recorded in EMR ( 7 ). The systematic inaccuracies in EMR documentation identified in this study likely reflect the depriorization of recording by healthcare professionals, a common occurrence in high-demand clinical environments ( 4 ). Owing to competing clinical responsibilities, real-time recording of oxygen flow adjustments is often impractical, leading to retrospective documentation after manual tasks are completed. As evidenced by the findings of this study, this delayed recording frequently results in omissions and inaccuracies. Underreporting of oxygen use in EMR has important implications, as accurate monitoring of oxygen therapy is essential for patient safety, resource allocation, and cost-effectiveness in hospitals. First, clinical decision-making may be compromised by the absence of reliable data on therapy duration and intensity, limiting the ability to optimize weaning protocols or assess treatment efficacy. Second, systematic underestimation of oxygen demand can undermine supply planning, a critical vulnerability during high-demand scenarios such as public health emergencies, thereby posing direct risks to patient safety. Finally, because EMR-derived data are often used for insurance reimbursement, underestimation of oxygen consumption may lead to substantial financial losses, with hospitals being compensated for only a fraction of the actual resources expended. Previous studies comparing automated and manual documentation of vital signs have consistently shown that manual methods are prone to transcription errors, delayed entries, and data omissions ( 6 , 8 ). In contrast, automated monitoring systems provide continuous and objective data acquisition, enhancing both the accuracy and timeliness of clinical information while reducing staff workload. By directly measuring and recording oxygen flow in real time, the ATAS O₂ system ensures complete traceability of oxygen therapy duration and dosage, parameters that are traditionally underreported in EMRs. Furthermore, real-time monitoring of oxygen consumption supports efficient, data-driven management of oxygen resources. The main limitation of this study is the absence of a gold standard for quantifying real oxygen consumption. Because medical oxygen is distributed through a centralized hospital pipeline system rather than through isolated delivery units, it is technically unfeasible to directly determine oxygen expenditure at the ward or bedside level. Nevertheless, the reliability of ATAS O₂ measurements is supported by its regulatory compliance with the responsible regulatory agency of Brazil (Agência Nacional de Vigilância Sanitária, ANVISA; register number 10349590139). This approval was granted following a technical report that included tests comparing pressure and flow values measured by the device against actual pressures and a gold-standard reference sensor, respectively. 5. Conclusion Oxygen consumption is systematically underreported in manual EMR documentation compared with automated monitoring by the ATAS O₂ system. In contrast, the continuous measurement of oxygen flow provided by ATAS O₂ yields a more accurate and reliable dataset for clinical management, resource planning, and financial accountability. Therefore, integrating automated monitoring technologies into hospital infrastructures represents a critical step toward enhancing patient safety and optimizing healthcare resource utilization. Abbreviations EMR, Electronic Medical Record ANVISA, Agência Nacional de Vigilância Sanitária Declarations Competing Interests Authors NPCG and RCFR are employees of Salvus, the company that developed the technology evaluated in this study. The company contributed solely to the study design and did not participate in data collection, data analysis, data interpretation, or manuscript preparation. The remaining authors declare no competing interests. Ethics approval : No clinical information or identifiable patient data was accessed at any point. Accordingly, the study met criteria for exemption from review by the institutional Research Ethics Committee. Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution DFMJ, CRL, JAS, NPCG, and RCFR were involved in the conception and original study design. ALMS was involved in the data collection. JAS organized the dataset for statistical analysis. VUM interpreted the statistical analysis and wrote the first manuscript draft. All authors contributed to reviewing and editing the final draft. The corresponding author attests that all listed authors meet authorship criteria and have approved the version to be published. Data Availability The datasets analyzed during the current study are available from the corresponding author on reasonable request. References Cousins JL, Wark PAB, McDonald VM. Acute oxygen therapy: a review of prescribing and delivery practices. Int J Chron Obstruct Pulmon Dis. 2016;11:1067–75. Malik MA. Fragility and challenges of health systems in pandemic: lessons from India’s second wave of coronavirus disease 2019 (COVID-19). Glob Health J. 2022;6(1):44–9. Arora N, Dennis A, Willson J, Norrie J, Tunstall M. Delivery of oxygen by standard oxygen flowmeters. Anaesthesia. 2021;76(11):1546–7. Olakotan O, Samuriwo R, Ismaila H, Atiku S. Usability Challenges in Electronic Health Records: Impact on Documentation Burden and Clinical Workflow: A Scoping Review. J Eval Clin Pract. 2025;31(4):e70189. Rosida NK, Rahmawati T, Wisana IDGH, Nosike M. Monitoring the Stability of Oxygen Flow Analyzer on Oxygen Station in the Hospital. ijeeemi. 2025;5(1):52–9. Wood J, Finkelstein J. Comparison of automated and manual vital sign collection at hospital wards. Stud Health Technol Inf. 2013;190:48–50. Tang W, Smith J, Dakkak J, Balasubramanian A, Seth B, Leotta C, et al. Decoding oxygen prescriptions: electronic health record documentation versus patient-reported use. BMC Pulm Med. 2024;24(1):491. Skyttberg N, Chen R, Koch S. Man vs machine in emergency medicine - a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires. BMC Emerg Med. 2018;18(1):54. Statements & Declarations Additional Declarations Competing interest reported. Authors NPCG and RCFR are employees of Salvus, the company that developed the technology evaluated in this study. The company contributed solely to the study design and did not participate in data collection, data analysis, data interpretation, or manuscript preparation. The remaining authors declare no competing interests. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Apr, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviews received at journal 03 Jan, 2026 Reviewers agreed at journal 24 Dec, 2025 Reviewers invited by journal 22 Dec, 2025 Editor invited by journal 10 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 08 Dec, 2025 First submitted to journal 08 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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01:19:08","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42844,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8280073/v1/05fd63d597186d4ba4a816f7.html"},{"id":99317390,"identity":"a6ad8cac-1d7e-496b-9e23-e07f8c376652","added_by":"auto","created_at":"2025-12-31 16:30:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison between EMR and ATAS O₂ in oxygen therapy monitoring across the study period. \u003c/strong\u003eThe number of beds using oxygen (left), number of recorded cycles (middle), and mean hours per cycle (right) are shown from September to January.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8280073/v1/d170d4bf27de4d0642a26bbe.jpeg"},{"id":99788329,"identity":"79201541-2bf0-4cd5-9961-e15854b7dd25","added_by":"auto","created_at":"2026-01-08 12:46:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":516720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8280073/v1/8bbc3a01-2002-4c90-8a78-7689228c93ff.pdf"}],"financialInterests":"Competing interest reported. Authors NPCG and RCFR are employees of Salvus, the company that developed the technology evaluated in this study. The company contributed solely to the study design and did not participate in data collection, data analysis, data interpretation, or manuscript preparation. The remaining authors declare no competing interests.","formattedTitle":"Automated Monitoring Reveals Underreporting of Oxygen Consumption in Electronic Medical Records","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOxygen is a fundamental medical resource, essential for sustaining patients in critical conditions such as pneumonia, sepsis, and acute respiratory failure (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Its central role in patient outcomes underscores the need for efficient supply and rational use. However, the COVID-19 pandemic exposed the fragility of healthcare infrastructures, as the unprecedented surge in demand led to global shortages of medical oxygen (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite being a vital and limited resource, oxygen is frequently misused in clinical practice, resulting in substantial financial and environmental burdens. Standard flowmeters, when fully opened beyond their calibrated range, can deliver flow rates of 65\u0026ndash;75 L/min, far exceeding therapeutic requirements (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In one study, 17 out of 21 patients received oxygen flows in excess of 15 L/min. The authors estimated that if only two patients were exposed to such excess flow, nearly 172,800 L of oxygen would be wasted per day, resulting in roughly 63\u0026nbsp;million liters of oxygen wasted annually (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEffective oxygen stewardship requires precise and continuous monitoring of consumption. However, traditional methods remain limited. Oxygen use is typically estimated from flow values manually entered into electronic medical records (EMR) by healthcare professionals, a time-consuming process prone to transcription errors (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). High clinical workloads often delay or preclude documentation, compromising the accuracy of consumption estimates. Moreover, manual flowmeters may lose calibration overtime due to wear, contamination, or inadequate maintenance (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), further reducing measurement precision. Finaly, because EMR-based oxygen consumption data often serve as the basis for billing health insurance providers, underreporting or inaccuracies can result in substantial financial losses, as hospitals may be reimbursed for only a portion of their actual oxygen usage. This highlights the need for more reliable monitoring systems to optimize both resource management and financial accountability.\u003c/p\u003e \u003cp\u003eAutomated monitoring technologies have emerged as a promising approach to overcome the limitations of manual documentation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Systems such as the ATAS O₂ (Salvus, Pernambuco, Brazil) enable continuous, objective measurement of oxygen flow, reducing reliance on manual recording and enhancing both clinical accuracy and operational efficiency. Despite their potential, few studies have systematically evaluated the performance of oxygen monitoring devices against EMR-based documentation under real-world hospital conditions, an essential step to validate their practical and clinical utility. To address this gap, this retrospective study compared oxygen consumption data recorded by the ATAS O₂ system with those documented manually in the EMR.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis retrospective study compared oxygen consumption data from 21 beds in the Coronary Step-Down Unit of Einstein Hospital Israelita, collected between September 2021 and January 2022, using two monitoring strategies: the ATAS O₂ (Salvus, Pernambuco, Brazil) device and the electronic medical record (EMR) entries registered by healthcare professionals. The analysis relied exclusively on administrative datasets generated weekly for internal billing verification, composed solely of anonymized medical record numbers and oxygen-therapy documentation status.\u003c/p\u003e \u003cp\u003eOxygen consumption data per bed were summarized into the following parameters: total consumption time (h), number of recorded cycles, mean hours per cycle (h), mean volume consumed per cycle (L), mean oxygen flow (L/min), and total volume consumed (L). To evaluate temporal patterns, data were aggregated monthly across the five study months.\u003c/p\u003e \u003cp\u003eStatistical analyses were conducted in SPSS (version 26.0) by the Institutional Department of Statistics. A significance level of 5% was adopted for all tests. Data normality was assessed using the Shapiro\u0026ndash;Wilk test and visual inspection of boxplots, histograms, and quantile\u0026ndash;quantile plots. Comparisons between monitoring methods were performed using either the Student\u0026rsquo;s t-test or the Mann\u0026ndash;Whitney U test, as appropriate.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe ATAS O₂ system recorded significantly higher total oxygen consumption time per bed compared with EMR documentation (median 533.19 vs. 48.20 h, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Surprisingly, ATAS O₂ detected a greater number of beds using oxygen than was documented in EMR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, left panel), indicating that oxygen use in some beds was not captured manually. This underreporting of beds in EMR partially explains the larger number of cycles recorded by ATAS O₂ (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, middle panel). Nevertheless, even when normalized per bed, ATAS O₂ still showed a consistently greater frequency of cycles compared with EMR (median 96 vs. 10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that the automated system captured more frequent changes in the oxygen flow.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of oxygen consumption metrics per bed recorded through Electronic Medical Records (EMR) and the ATAS O₂ device\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATAS O₂\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal consumption time (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.20 (20.80, 345.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e533.19 (277.91, 778.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of recorded cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.00 (5.00, 22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.00 (62.00, 207.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean hours per cycle (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.64\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.082\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean volume consumed per cycle (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269.33 (90.00, 584.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58 (0.36, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean oxygen flow (L/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58 (0.35, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19 (1.46, 2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal volume consumed (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84 (0.94, 7.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.41 (41.68, 111.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (Q1, Q3). *Mann-Whitney U test, \u0026dagger;Student's t-test.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn line with these observations, the mean duration per cycle was longer in EMR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, right panel), starting above 30 hours in September and progressively decreasing throughout the study period, whereas ATAS O₂ reported consistently shorter and more stable cycle durations (approximately 5 hours per cycle). Across the study period, the mean hours per cycle were also lower with ATAS O₂ (6.64\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20 h) compared with EMR (11.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07 h), although this difference did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.082; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsistent with the prolonged cycle times, EMR documentation showed a higher oxygen volume consumed per cycle (median 269.33 vs 0.58 L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, ATAS O₂ detected significantly higher mean oxygen flow rates compared with EMR (median 2.19 vs. 0.58 L/min, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that manual documentation underestimates flow intensities during oxygen therapy. In agreement with this finding, the total volume consumed per bed recorded by ATAS O₂ was markedly higher compared with EMR (median 82.41 vs. 1.84 L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, oxygen consumption was consistently underreported in manual electronic medical record (EMR) documentation compared with automated monitoring by the ATAS O₂ system. The ATAS O₂ not only captured longer total durations of oxygen use but also identified a greater number of beds receiving oxygen therapy. Since both systems monitored the same 21 beds, this discrepancy indicates that oxygen use in some patients was not documented in the EMR at all. Manual underreporting also led to longer recorded cycle durations, lower flow rates, and a substantially lower total volume of oxygen consumption per bed compared with ATAS O₂ data. These findings align with previous reports showing significant gaps in the documentation of oxygen prescriptions and patient-reported oxygen use, where only 45% of prescribed oxygen values were accurately recorded in EMR (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe systematic inaccuracies in EMR documentation identified in this study likely reflect the depriorization of recording by healthcare professionals, a common occurrence in high-demand clinical environments (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Owing to competing clinical responsibilities, real-time recording of oxygen flow adjustments is often impractical, leading to retrospective documentation after manual tasks are completed. As evidenced by the findings of this study, this delayed recording frequently results in omissions and inaccuracies.\u003c/p\u003e \u003cp\u003eUnderreporting of oxygen use in EMR has important implications, as accurate monitoring of oxygen therapy is essential for patient safety, resource allocation, and cost-effectiveness in hospitals. First, clinical decision-making may be compromised by the absence of reliable data on therapy duration and intensity, limiting the ability to optimize weaning protocols or assess treatment efficacy. Second, systematic underestimation of oxygen demand can undermine supply planning, a critical vulnerability during high-demand scenarios such as public health emergencies, thereby posing direct risks to patient safety. Finally, because EMR-derived data are often used for insurance reimbursement, underestimation of oxygen consumption may lead to substantial financial losses, with hospitals being compensated for only a fraction of the actual resources expended.\u003c/p\u003e \u003cp\u003ePrevious studies comparing automated and manual documentation of vital signs have consistently shown that manual methods are prone to transcription errors, delayed entries, and data omissions (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In contrast, automated monitoring systems provide continuous and objective data acquisition, enhancing both the accuracy and timeliness of clinical information while reducing staff workload. By directly measuring and recording oxygen flow in real time, the ATAS O₂ system ensures complete traceability of oxygen therapy duration and dosage, parameters that are traditionally underreported in EMRs. Furthermore, real-time monitoring of oxygen consumption supports efficient, data-driven management of oxygen resources.\u003c/p\u003e \u003cp\u003eThe main limitation of this study is the absence of a gold standard for quantifying real oxygen consumption. Because medical oxygen is distributed through a centralized hospital pipeline system rather than through isolated delivery units, it is technically unfeasible to directly determine oxygen expenditure at the ward or bedside level. Nevertheless, the reliability of ATAS O₂ measurements is supported by its regulatory compliance with the responsible regulatory agency of Brazil (Ag\u0026ecirc;ncia Nacional de Vigil\u0026acirc;ncia Sanit\u0026aacute;ria, ANVISA; register number 10349590139). This approval was granted following a technical report that included tests comparing pressure and flow values measured by the device against actual pressures and a gold-standard reference sensor, respectively.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOxygen consumption is systematically underreported in manual EMR documentation compared with automated monitoring by the ATAS O₂ system. In contrast, the continuous measurement of oxygen flow provided by ATAS O₂ yields a more accurate and reliable dataset for clinical management, resource planning, and financial accountability. Therefore, integrating automated monitoring technologies into hospital infrastructures represents a critical step toward enhancing patient safety and optimizing healthcare resource utilization.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEMR, Electronic Medical Record\u003c/p\u003e\n\u003cp\u003eANVISA, Ag\u0026ecirc;ncia Nacional de Vigil\u0026acirc;ncia Sanit\u0026aacute;ria\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eAuthors NPCG and RCFR are employees of Salvus, the company that developed the technology evaluated in this study. The company contributed solely to the study design and did not participate in data collection, data analysis, data interpretation, or manuscript preparation. The remaining authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eEthics approval\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eNo clinical information or identifiable patient data was accessed at any point. Accordingly, the study met criteria for exemption from review by the institutional Research Ethics Committee.\u003c/p\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDFMJ, CRL, JAS, NPCG, and RCFR were involved in the conception and original study design. ALMS was involved in the data collection. JAS organized the dataset for statistical analysis. VUM interpreted the statistical analysis and wrote the first manuscript draft. All authors contributed to reviewing and editing the final draft. The corresponding author attests that all listed authors meet authorship criteria and have approved the version to be published.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCousins JL, Wark PAB, McDonald VM. Acute oxygen therapy: a review of prescribing and delivery practices. Int J Chron Obstruct Pulmon Dis. 2016;11:1067\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik MA. Fragility and challenges of health systems in pandemic: lessons from India\u0026rsquo;s second wave of coronavirus disease 2019 (COVID-19). Glob Health J. 2022;6(1):44\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArora N, Dennis A, Willson J, Norrie J, Tunstall M. Delivery of oxygen by standard oxygen flowmeters. Anaesthesia. 2021;76(11):1546\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlakotan O, Samuriwo R, Ismaila H, Atiku S. Usability Challenges in Electronic Health Records: Impact on Documentation Burden and Clinical Workflow: A Scoping Review. J Eval Clin Pract. 2025;31(4):e70189.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosida NK, Rahmawati T, Wisana IDGH, Nosike M. Monitoring the Stability of Oxygen Flow Analyzer on Oxygen Station in the Hospital. ijeeemi. 2025;5(1):52\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood J, Finkelstein J. Comparison of automated and manual vital sign collection at hospital wards. Stud Health Technol Inf. 2013;190:48\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang W, Smith J, Dakkak J, Balasubramanian A, Seth B, Leotta C, et al. Decoding oxygen prescriptions: electronic health record documentation versus patient-reported use. BMC Pulm Med. 2024;24(1):491.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkyttberg N, Chen R, Koch S. Man vs machine in emergency medicine - a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires. BMC Emerg Med. 2018;18(1):54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatements \u0026amp; Declarations\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Oxygen inhalation therapy, Oxygen consumption monitoring, Electronic health records, Health resources utilization, Automation","lastPublishedDoi":"10.21203/rs.3.rs-8280073/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8280073/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate monitoring of oxygen therapy is essential for patient safety, resource management, and financial accountability. However, oxygen consumption is often underreported in manual electronic medical record (EMR) documentation. Automated monitoring technologies, such as ATAS O₂ (Salvus, Pernambuco, Brazil), have emerged as a promising solution to this limitation. This retrospective study compared oxygen consumption data recorded in the EMR with measurements obtained using ATAS O₂ across 21 hospital beds over a five-month period. The ATAS O₂ system detected a higher number of beds receiving oxygen, total consumption time per bed (median 533 vs. 48 h, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), frequency of cycles (median 96 vs. 10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), mean of oxygen flow rates (median 2.19 vs. 0.58 L/min, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and total volume consumed per bed (median 82 vs. 1.84 L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, EMR documentation captured longer mean duration per cycle (11.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07 vs 6.64\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20 h, p\u0026thinsp;=\u0026thinsp;0.082) and higher oxygen volume consumed per cycle (median 269 vs 0.58 L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings indicate systematic underreporting in manual EMR documentation and highlight the potential benefits of automated monitoring of oxygen therapy in improving accuracy, optimizing hospital resource management, and supporting financial oversight.\u003c/p\u003e","manuscriptTitle":"Automated Monitoring Reveals Underreporting of Oxygen Consumption in Electronic Medical Records","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 01:19:03","doi":"10.21203/rs.3.rs-8280073/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-03T07:59:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T22:52:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301634378895216464469571336785025573482","date":"2026-02-02T14:41:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T08:11:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21703240903484020710964112711200064537","date":"2025-12-24T23:50:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-22T12:39:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-11T04:10:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T12:13:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-08T12:52:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Health Systems","date":"2025-12-08T12:43:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e123327a-581c-4460-b55c-af60404d92b4","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T05:25:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 01:19:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8280073","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8280073","identity":"rs-8280073","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}