The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
A new sociotechnical model for studying health information technology in complex adaptive healthcare systems
Open accessAbstract
BACKGROUND: Conceptual models have been developed to address challenges inherent in studying health information technology (HIT). METHOD: This manuscript introduces an eight-dimensional model specifically designed to address the sociotechnical challenges involved in design, development, implementation, use and evaluation of HIT within complex adaptive healthcare systems. DISCUSSION: The eight dimensions are not independent, sequential or hierarchical, but rather are interdependent and inter-related concepts similar to compositions of other complex adaptive systems. Hardware and software computing infrastructure refers to equipment and software used to power, support and operate clinical applications and devices. Clinical content refers to textual or numeric data and images that constitute the 'language' of clinical applications. The human--computer interface includes all aspects of the computer that users can see, touch or hear as they interact with it. People refers to everyone who interacts in some way with the system, from developer to end user, including potential patient-users. Workflow and communication are the processes or steps involved in ensuring that patient care tasks are carried out effectively. Two additional dimensions of the model are internal organisational features (eg, policies, procedures and culture) and external rules and regulations, both of which may facilitate or constrain many aspects of the preceding dimensions. The final dimension is measurement and monitoring, which refers to the process of measuring and evaluating both intended and unintended consequences of HIT implementation and use. We illustrate how our model has been successfully applied in real-world complex adaptive settings to understand and improve HIT applications at various stages of development and implementation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Digitisation redistributes responsibility across human and technical actors.”
From Why digital health fails silently: a sociotechnical theory of health information technology–related risk (2026) · cites this paper as [13]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Digital systems frequently mediate clinical work in ways that reduce direct visibility of system state and process integrity.”
From Why digital health fails silently: a sociotechnical theory of health information technology–related risk (2026) · cites this paper as [13]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This diffusion complicates accountability and can create gaps in oversight, particularly when responsibilities for monitoring, escalation, and remediation are not clearly defined (13).”
From Why digital health fails silently: a sociotechnical theory of health information technology–related risk (2026) · cites this paper as [13]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Decisions about information accuracy, timing, and relevance are increasingly mediated by algorithms, system configurations, and interoperability rules that are often opaque to end users (1, 13).”
From Why digital health fails silently: a sociotechnical theory of health information technology–related risk (2026) · cites this paper as [13]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The interest in trialing ambient AI scribes in the ICU was tempered by concerns about the sociotechnical implications of voice-based AI in team-based, high-risk environments, highlighting the need for careful, context-sensitive implementation [27].”
From Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study (2026) · cites this paper as [27]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The interview guide was previously developed based on sociotechnical theory and will explore the following key constructs (Table 3): perceptions of usefulness, resource needs and constraints, barriers and facilitators to implementation, culture of safety, and adaptations [13,23].”
From Improving Communication in Intensive Care Unit to Ward Transitions: Protocol for Multisite National Implementation of the ICU-PAUSE Handoff Tool (2023) · cites this paper as [13]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A coding framework rooted in the 8 dimensions of sociotechnical theory has been developed along the following codes: hardware and software, clinical content, human-computer interface, people, workflow and communication, internal organizational features, and external rules and regulations [13].”
From Improving Communication in Intensive Care Unit to Ward Transitions: Protocol for Multisite National Implementation of the ICU-PAUSE Handoff Tool (2023) · cites this paper as [13]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Existing approaches, including sociotechnical models and safety frameworks, have provided important insights into the role of system interactions and organisational context (13, 16, 17, 28, 48).”
From Why digital health fails silently: a sociotechnical theory of health information technology–related risk (2026) · cites this paper as [13]
The cited source says what the claim attributes to it.
In the citing paper
“This study is informed by sociotechnical theory, which offers a paradigm for studying health information technology innovations [13].”
From Improving Communication in Intensive Care Unit to Ward Transitions: Protocol for Multisite National Implementation of the ICU-PAUSE Handoff Tool (2023) · cites this paper as [13]
The cited source says what the claim attributes to it.
In the citing paper
“This convergent mixed method approach will allow us to quantify the feasibility and acceptability of the ICU-PAUSE tool while also qualitatively assessing user experiences with the tool using the established implementation frameworks of CFIR and sociotechnical theory [13,14,17].”
From Improving Communication in Intensive Care Unit to Ward Transitions: Protocol for Multisite National Implementation of the ICU-PAUSE Handoff Tool (2023) · cites this paper as [13]
The cited source says what the claim attributes to it.
In the citing paper
“We recognize that user interaction, the EHR interface, institutional workflows, and regulatory policies are all dynamic and interdependent.”
From Improving Communication in Intensive Care Unit to Ward Transitions: Protocol for Multisite National Implementation of the ICU-PAUSE Handoff Tool (2023) · cites this paper as [13]
In the citing paper
“Detecting and preventing EHR-related safety concerns is challenging because concerns are often multifaceted, involving not only potentially unsafe technological features of the EHR but also EHR user behaviors, organizational characteristics, and rules and regulations that guide EHR-related activities.”
From An analysis of electronic health record-related patient safety concerns (2014) · cites this paper as [24]
In the citing paper
“For instance, we previously developed a sociotechnical model that proposes eight interdependent dimensions that are essential to understand EHR-related safety (table 1; Sittig and Singh model).24 34”
From An analysis of electronic health record-related patient safety concerns (2014) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · Routledge eBooks · doi:10.4324/9780203710753-35 · Cited by 37,670
Hutchins, E. (1995) · doi:10.7551/mitpress/1881.001.0001 · Cited by 10,801
Human error: models and management
Reason, J. (2000) · BMJ · doi:10.1136/bmj.320.7237.768 · Cited by 5,665
Designing the user interface: Strategies for effective human-computer interaction
(1993) · Applied Ergonomics · doi:10.1016/0003-6870(93)90471-k · Cited by 5,592
The Psychology of Everyday Things
Massaro, D. W., & Norman, D. A. (1990) · The American Journal of Psychology · doi:10.2307/1423268 · Cited by 3,484
Risk management in a dynamic society: a modelling problem
Rasmussen, J. (1997) · Safety Science · doi:10.1016/s0925-7535(97)00052-0 · Cited by 2,994
The Technology Acceptance Model: Its past and its future in health care
Holden, R. J., & Karsh, B.-T. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2009.07.002 · Cited by 2,669
Diffusion of Innovations, 5th Edition
Rogers, E. M., Simon, 。., & Schuster (2003) · Cited by 2,049
Work system design for patient safety: the SEIPS model
Carayon, P., Schoofs Hundt, A., Karsh, B.-T., Gurses, A. P., Alvarado, C. J., Smith, M., & Flatley Brennan, P. (2006) · Quality in Health Care · doi:10.1136/qshc.2005.015842 · Cited by 1,795 · cited 1×
“Third, Carayon's Systems Engineering Initiative for Patient Safety model”
Flagged first, then by citations
Research works printed but not linked: 1
- [36]Sittig DF Wright A Simonaitis L Carpenter JD Allen GO Doebbeling BN Sirajuddin AM Ash JS Middleton B The state of the art in clinical knowledge management: an inventory of tools and techniques Int J Med Inform 2010 Jan 79 1 44 57 19828364 PMC2895508
Cited by
Sorted by citations
Items 801–892 of 892
From Realism to Relevance: Rethinking Simulator Fidelity in the Era of Generative AI
Schoenherr, J. R. (2026) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2026.3693463
Song, Y. A., Kim, H. J., Park, J. Y., Shin, S. J., Bae, S. Y., & Lim, J. (2026) · doi:10.2196/preprints.105217
Ikua, A. W., Mwavita, M., & Ekpudu, J. (2026) · doi:10.2196/preprints.105249
Thayer, J. G., Shaeffer, G. P., Fiks, A. G., Jenssen, B. P., Ramachandran, J., Karavite, D., Nabi-Burza, E., Winickoff, J. P., & Grundmeier, R. W. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag114
Issah, M., & Adam, I. O. (2026) · Strategic Business Research · doi:10.1016/j.sbr.2026.100215
Lahiri, R. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1881783 · Cited by 1
Natalia, D., Nesia, W. S., Wuisan, D. S. S., & Antonio, F. (2026) · Research Square · doi:10.21203/rs.3.rs-9609691/v1
Automating Aspiration Precautions to Reduce Latent Safety Risk in the Emergency Department
Sangal, R. B., Pavuluri, S. K., Sather, J., Haggan, J., Parwani, V. L., Cahill, J. C., Davison, C., Mittleman, C., Tucker, C., Dubin, S., et al. (2026) · The Joint Commission Journal on Quality and Patient Safety · doi:10.1016/j.jcjq.2026.07.003
Sociotechnical safety risks in oncology: a framework-guided qualitative study
Recsky, C., Tam, M. B., Heywood, A., Dillon, R. A., Ronquillo, C., Lauck, S., & Lambert, L. K. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag103
AI Guides Attention, But What Should Educators See? Designing Human-AI Decision Support in Education
Köroğlu, M. N., & Wintersberger, P. (2026) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3805029.3818297
Babamahmoodi, A., Babamahmoodi, F., & Marjani, M. (2026) · International Journal of Infection · doi:10.5812/iji-169286
Verulava, T. (2026) · Home Health Care Management & Practice · doi:10.1177/10848223261465411
Najar, T., Zammel, I. B., & Kort, W. (2026) · VINE Journal of Information and Knowledge Management Systems · doi:10.1108/vjikms-03-2026-0123
Dhodho, E., Choga, K., Mundoga, F., Chimberengwa, P. T., Gongora, R., Chaurura, T., Webb, K. A., Benjamin, M., Chinyanga, T. T., Banda, F., et al. (2026) · medRxiv · doi:10.64898/2026.07.16.26357920
Shahin, A., & Masry, B. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1854701
Damalie, E., Suomi, R., & Mahmoud, M. (2026) · JMIR Medical Informatics · doi:10.2196/84820
Organizational factors influencing the adoption of AI technologies in preventive healthcare
Akar, E., Bozanta, A., & Hakyemez, T. C. (2026) · Journal of Health Organization and Management · doi:10.1108/jhom-10-2025-0647
Deng, Y., Zhu, Y., Li, J., & Xiong, Y. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1917065
From Code to Clinic: Bridging the Implementation Gap in AI-Based Clinical Decision Support
Savadkoohi, E. G., Asadi, F., & Emami, H. (2026) · International Interdisciplinary Conference on Artificial Intelligence: Engineering, Health, Finance and Humanities (IICAI) · doi:10.1109/iicai70155.2026.11620638
Cao, Y., Tang, Y., Deng, L., Liu, X., Feng, Z., & Gao, Y. (2026) · Journal of Advanced Nursing · doi:10.1111/jan.70691
Karimian, S., Corrigan, S., Ward, M. E., Geary, U., & Brennan, R. (2026) · Springer series in design and innovation · doi:10.1007/978-3-032-23073-7_7
Yiadom, M. Y. A. B. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2699706
Kierkegaard, P., Su, B., Gujral, D. M., Liesching-Schroder, K., Lagergren, P., McGregor, A. H., & Grayer, J. (2026) · npj Health Systems · doi:10.1038/s44401-026-00125-9
Outils 3.0 en 2050 : quand le cadre de santé manage avec l’intelligence artificielle
Niggel, A. (2026) · Soins Cadres · doi:10.1016/j.scad.2026.06.011
Bekele, W. S. (2026) · Research Square · doi:10.21203/rs.3.rs-10630810/v1
Artificial Intelligence and Nursing: Freedom From the Mundane or Subservience to a New Nobility
Richards, D. A. (2026) · Journal of Advanced Nursing · doi:10.1111/jan.70726
Hsu, L.-C., Weng, Y., & Liu, W. (2026) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.70334
Vernic, C., Bedreag, O. H., Suliţanu, D. C., Petre, I., Muntean, L. I., & Ursoniu, S. (2026) · Nursing Reports · doi:10.3390/nursrep16080290
Mallieswari, R., & Shubha, B. N. (2026) · Discover Sustainability · doi:10.1007/s43621-026-04296-1
The AI Adaptation Paradox: Navigating Professional Relevance and Sustainable Integration in Nursing
Silva, A. (2026) · Nursing Inquiry · doi:10.1111/nin.70158
The Twin Heartbeats of Leadership
Shirley, E., & Migliore, L. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7709-4.ch008
Lipskiy, N., & Flowerday, S. (2026) · Computers · doi:10.3390/computers15100717
From promise to practice: digital health through the lens of neonatal care
Variane, G. F. T., Leandro, D. M. K., Mimiça, M. J., Austin, T., Chock, V. Y., Craig, A. K., Mohammad, K., & Meurs, K. P. V. (2026) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2026.101600
Sikki, N., Sari, R. R., Nabila, D., Putri, A. S., & Naja, N. S. (2026) · Journal of Management and Digital Business · doi:10.53088/jmdb.v6i2.3016
Osei-Donkor, B., Hassoun, A., & Jabin, M. S. R. (2026) · Frontiers in Health Services · doi:10.3389/frhs.2026.1872556
Dormechele, W., Guven‐Uslu, P., & de la Iglesia, B. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106678
Su, H., zou, Y., Fang, W., Hu, Y., Zheng, Y., Zhang, Q., & Qin, W. (2026) · Asian Nursing Research · doi:10.1016/j.anr.2026.09.002
Abdullahzadeh, M. (2026) · Digital Health · doi:10.1177/20552076261477990
Darla, M., Miltz, D., Chandnani, K., Purkayastha, S., Diehl, J. W., & Bhavani, S. V. (2026) · JMIR Human Factors · doi:10.2196/95472
Wang, M.-H., Chen, Y.-T., Wu, H.-Y., Shih, P., Tsung-Neng, T., Li, W., & Wang, K.-Y. (2026) · doi:10.2196/preprints.111342
Designing Portable Audit Evidence for Health Information Exchange
Clim, A. (2026) · Journal of Computer Information Systems · doi:10.1080/08874417.2026.2720000
Danilczuk, D., Półchłopek, S., Szpanelewski, K., Kołodziej, R., Kołodziej, A., Lulek, S., Klecha, J., Kucab, E., Dziki, K., Kula, I. K., et al. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.2(50).2026.5713
Galarza, A. B., Gamarra-Moreno, A., Bautista, W. E. Q., & Guillén, J. A. R. (2026) · Systems · doi:10.3390/systems14091124
Butler, J. M., Jones, M., Balbin, C. A., Taber, P. A., Fiol, G. D., Dalrymple, R., & Kawamoto, K. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag091
Saab, A., Sokhn, E. S., & Morr, C. E. (2026) · Springer eBooks · doi:10.1007/978-3-032-30647-0_22
Sebin, B., & Sebin, I. K. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1956533
Ornelas, J. C. O. (2026) · Nexus Science Review · doi:10.66104/tbe64a08
Omar, I. A., Simsekler, M. C. E., Salah, K., & Omar, M. (2026) · Safety Science · doi:10.1016/j.ssci.2026.107458
Balakrishnan, M. S., Khan, Z. R., Mayumu, N., Krásničan, V., Rosenthal, L., Willis, M., & Reed, R. H. (2026) · doi:10.2196/preprints.111731
Operationalizing ethical AI for safe clinical integration: A qualitative interview study
ÇAYIRTEPE, Z., Demir, B., & Ertuğrul, Ö. F. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106721
Moghadam, S. T., & Jabin, M. S. R. (2026) · doi:10.2196/preprints.111956
Yang, J., Tai, J., & Dong, C. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1951971
Zhou, G., & Wu, L. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1923445
Ham, P. (2026) · American Journal of Occupational Therapy · doi:10.1177/19437676261481400
Chung, J., Park, Y.-E., Kim, Y., Lee, J., & Lee, Y. (2026) · JMIR Medical Informatics · doi:10.2196/95500
Accountable Action End Points for Evaluating Artificial Intelligence in Digital Public Health
Zhou, X., Liu, H., & Hua, Z. (2026) · Journal of Medical Internet Research · doi:10.2196/106243
Pilosof, N. P., Yoeli‐Ullman, R., Toussia‐Cohen, S., Galler, E., Barkai, G., Zimlichman, E., & Tsur, A. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1797655
Badawy, W., & Shaban, M. (2026) · Applied Nursing Research · doi:10.1016/j.apnr.2026.152147
Yao, L., Deng, Z., Luo, F., & He, L. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1894293
Baidya, M., & Müller, S. D. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-30982-2_9
Matsangidou, M., Kowal, M. R., Yianni, J., Krini, M., Pathak, S., Gordon, K., Luker, M. A., Stocken, D. D., Jayne, D. G., & Pattichis, C. S. (2026) · JMIR Human Factors · doi:10.2196/86725
ЦИФРОВІЗАЦІЯ УПРАВЛІННЯ ОХОРОНОЮ ЗДОРОВ’Я В УКРАЇНІ
Тарасюк, А. Ю. (2026) · Вісник Пенітенціарної асоціації України · doi:10.34015/2523-4552.2026.3.18
Oluwole, O. O., Bishop, K. R., & Goldszmidt, M. A. (2026) · doi:10.2196/preprints.112449
Dieu, A. N. T., & Ngoc, H. L. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1928965
Brobbey, A. A., Bhosale¹, N., & Ackom, J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1864565
Vernic, C., Bedreag, O. H., Suliţanu, D. C., Muntean, L. I., & Ursoniu, S. (2026) · Healthcare · doi:10.3390/healthcare14193306
Conway, A., Hada, A., Schluter, J., Xu, H. G., Lowden, D., Miller, T., Donald, K., & Teodorczuk, A. (2026) · JMIR Nursing · doi:10.2196/106133
Detels, L. M., Zhai, M., Cai, Z., & Trenz, M. (2026) · npj Health Systems · doi:10.1038/s44401-026-00154-4
Singh, A. J., Singh, A., Kumar, S., Dhelim, S., & Singh, C. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106760
An interaction perspective on situation awareness in human–AI teaming: A scoping review
Wang, M., Chen, T., & Or, C. K. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101380
McCabe, I., & O'Keeffe, D. T. (2026) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2026.100428
“Most of what I do is a workaround now”: Clinician Use of Workarounds Following an EHR Transition
Shirley, S. E., Sulc, C., Cole, E., Sterling, R., Lin, Y.-L., Obara, E., Shirley, S., Wong, E. S., Rinne, S. T., & Young, J. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-026-10840-6
Adapa, K., Garg, M., Sethi, R., Venketa, A. Y., Chauhan, T., Regi, M., & Mazur, Ł. (2026) · Asia-Pacific Journal of Oncology Nursing · doi:10.1016/j.apjon.2026.101044
Human Oversight Is Not Enough: Rethinking Accountability in AI-Assisted Clinical Decision-Making
Ren, Y. (2026) · Health Nexus Digital Health and Medical AI · doi:10.63802/hndh.v1.i1.362
Stochastic Processes for Adaptive Healthcare Systems
Sachan, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9705-4.ch001
Quality Management in Healthcare: Concepts, Frameworks, Measurement, and Improvement
Štarc, J. (2026) · Encyclopedia · doi:10.3390/encyclopedia6100230
Criscuolo, S., Pesola, M., Bozzetto, L., & Arpaïa, P. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-41230-0_33
Governança Tecnológica e Gestão de Projetos na Implantação de Sistemas de Informação para Saúde
Aires, G. V. (2026) · RCMOS - Revista Científica Multidisciplinar O Saber · doi:10.51473/rcmos.v1i2.2026.3045
Aires, G. V. (2026) · RCMOS - Revista Científica Multidisciplinar O Saber · doi:10.51473/rcmos.v1i1.2026.3044
Singh, S., & Firmin, S. (2026) · Computers · doi:10.3390/computers15100729
Deobhakta, A. A., & Rosen, R. B. (2026) · JMIR Medical Informatics · doi:10.2196/101576
Towards an Adaptive and Intelligent Dashboard for AI-Driven Clinical Decision Support
Orji, F. A., Motolani, A., Ataguba, G., Rudzicz, F., & Orji, R. (2026) · IEEE International Conference on Intelligent Systems · doi:10.1109/is71416.2026.11727218
The value of open IT standards for a national healthcare system: The case of Germany
Whitaker, J., Thompson, S., Raptis, G., & Westner, M. (2026) · Health Policy and Technology · doi:10.1016/j.hlpt.2026.101343
From clinician review to measurable human oversight of AI-drafted paediatric discharge documentsPreprint
Morquin, D., Bochu, G., Robert, L., Loi, Z., Buhnila, I., Ferreira, R., Jeziorski, É., Messaoui, L., Piarroux, R., & Yauy, K. (2026) · medRxiv · doi:10.64898/2026.10.20.26365977
Power, D., & Power, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7669112
Artificial intelligence early-warning systems for sepsis: From risk scores to bedside action
Yu, Z., & Chuan, S. (2026) · Technology and Health Care · doi:10.1177/09287329261502097
Pereira, K. C. (2026) · Nexus Science Review · doi:10.66104/cb31g085
AI in Long-Term Care: A Closed-Loop Sociotechnical Reliability Framework
Min, K., & Min, J. (2026) · Journal of Medical Internet Research · doi:10.2196/105161
Jabin, M. S. R., Nyatuka, R. D., & Macharia, P. M. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1939519
Healthcare Stakeholder Transformation Mapping by 2036: A Qualitative Foresight Study (Preprint)Preprint
Árvai, N., Katonai, G., & Meskó, B. (2026) · doi:10.2196/preprints.115604
Metadata
- Authors
- Sittig, D. F., & Singh, H.
- Affiliations
- Memorial Hermann (US)
- Michael E. DeBakey VA Medical Center (US)
- Corresponding author
- Sittig, D. F.
- Venue
- Quality and Safety in Health Care
- Publisher
- BMJ
- Published
- 2010-10-01
- Volume
- 19
- Issue
- Suppl 3
- Pages
- i68-i74
- ISSN
- 1475-3898 (ISSN-L)
- 1475-3901
- PMC
- PMC3120130
- OpenAlex
- W2110034024
- Cited by
- 897
- References
- 64
- Citations by year
- 2026: 179 · 2025: 91 · 2024: 43 · 2023: 75 · 2022: 58 · 2021: 66 · 2020: 52 · 2019: 70 · 2018: 40 · 2017: 48 · 2016: 41 · 2015: 36 · 2014: 36 · 2013: 34 · 2012: 15
- FWCI
- 36.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- BMJ Quality & Safety (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Health Services Research and Development
- National Institutes of Health
- Agency for Healthcare Research and Quality (R18HS017820)
- VA National Center for Patient Safety
- National Cancer Institute (K23CA125585)
- U.S. National Library of Medicine (R01LM006942, R56LM006942)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- health information technology, socio-technical model, complex adaptive systems, health care system, clinical workflow, human-machine interaction, organizational culture, health policy and regulation, monitoring and measurement, unintended consequences
- MeSH terms
- Diffusion of Innovation, Health Services Research, Humans, Ergonomics, Medical Informatics, Models, Theoretical, Planning Techniques, Programming Languages, Software Design, User-Computer Interface, Organizational Culture, Organizational Policy, Workflow
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
