Adaptation and Validation of the Situation Awareness Global Assessment Technique for Student Registered Nurse Anesthetists

Anesthesia is a health care specialty fraught with high workload demands, stressful work environments, increased production pressure, work areas with many distractions, an increasing use of advanced technology, and the constant need to prioritize work actions. Effective clinical judgment in this dynamic environment necessitates that the provider demonstrate the ability to project what may occur secondary to actual or potential condition changes. These key elements operationalize situation awareness (SA). High level SA is an important characteristic for the successful development of student registered nurse anesthetists (SRNAs). With Endsley’s “Theory of Situation Awareness” as the foundation, the goal of this study was to adapt and validate the “Situation Awareness Global Assessment Technique” (SAGAT), to quantify SRNAs\u27 SA during a specific simulated anesthesia event. With IRB approval, purposeful sampling identified a group of CRNA, nurse educator subjects and an exploratory sequential mixed methods design utilized. Delphi methods during qualitative data collection and validation used a seven-member sample. Content analysis resulted in items for the adapted SAGAT. Quantitative methods utilized data collected from a second 40-member sample yielding item content validity and scale content validity indices (S-CVI/Ave. 0.92). Additionally, exploratory factor analysis provided further reliability with a Cronbach’s alpha of 0.937. Findings revealed that a SAGAT specific to the anesthesia domain and the SRNA subgroup was amenable to adaptation and validation, providing positive implications in SRNA education and training. Additionally, results support the further adaptation, validation, and use of this instrument in other anesthetic content areas, as well as other health care domains

Good practices for clinical data warehouse implementation: a case study in France

Real World Data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case study of the 32 French regional and university hospitals governance, we highlight key aspects of modern Clinical Data Warehouses (CDWs): governance, transparency, types of data, data reuse, technical tools, documentation and data quality control processes. Semi-structured interviews as well as a review of reported studies on French CDWs were conducted in a semi-structured manner from March to November 2022. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The implementation of CDW in France dates from 2011 and accelerated in the late 2020. From this case study, we draw some general guidelines for CDWs. The actual orientation of CDWs towards research requires efforts in governance stabilization, standardization of data schema and development in data quality and data documentation. Particular attention must be paid to the sustainability of the warehouse teams and to the multi-level governance. The transparency of the studies and the tools of transformation of the data must improve to allow successful multi-centric data reuses as well as innovations in routine care.Comment: 16 page

Virtual Assisted Self Interviewing (VASI): An Expansion of Survey Data Collection Methods to the Virtual Worlds by Means of VDCI

Changes in communication technology have allowed for the expansion of data collection modes in survey research. The proliferation of the computer has allowed the creation of web and computer assisted auto-interview data collection modes. Virtual worlds are a new application of computer technology that once again expands the data collection modes by VASI (Virtual Assisted Self Interviewing). The Virtual Data Collection Interface (VDCI) developed at Indiana University in collaboration with the German Socio-Economic Panel Study (SOEP) allows survey researchers access to the population of virtual worlds in fully immersive Heads-up Display (HUD)-based survey instruments. This expansion needs careful consideration for its applicability to the researcher's question but offers a high level of data integrity and expanded survey availability and automation. Current open questions of the VASI method are an optimal sampling frame and sampling procedures within e. g. a virtual world like Second Life (SL). Further multi-modal studies are proposed to aid in evaluating the VDCI and placing it in context of other data collection modes.Interviewing mode, PAPI, CAPI, CASI, VASI, VDCI, second life

Virtual Assisted Self Interviewing (VASI): An Expansion of Survey Data Collection Methods to the Virtual Worlds by Means of VDCI

Changes in communication technology have allowed for the expansion of data collection modes in survey research. The proliferation of the computer has allowed the creation of web and computer assisted auto-interview data collection modes. Virtual worlds are a new application of computer technology that once again expands the data collection modes by VASI (Virtual Assisted Self Interviewing). The Virtual Data Collection Interface (VDCI) developed at Indiana University in collaboration with the German Socio-Economic Panel Study (SOEP) allows survey researchers access to the population of virtual worlds in fully immersive Heads-up Display (HUD)-based survey instruments. This expansion needs careful consideration for its applicability to the researcher’s question but offers a high level of data integrity and expanded survey availability and automation. Current open questions of the VASI method are an optimal sampling frame and sampling procedures within e. g. a virtual world like Second Life (SL). Further multimodal studies are proposed to aid in evaluating the VDCI and placing it in context of other data collection modes.Interviewing Mode, PAPI, CAPI, CASI, VASI, VDCI, Second Life

Advanced multiparametric optimization and control studies for anaesthesia

Anaesthesia is a reversible pharmacological state of the patient where hypnosis, analgesia and muscle relaxation are guaranteed and maintained throughout the surgery. Analgesics block the sensation of pain; hypnotics produce unconsciousness, while muscle relaxants prevent unwanted movement of muscle tone. Controlling the depth of anaesthesia is a very challenging task, as one has to deal with nonlinearity, inter- and intra-patient variability, multivariable characteristics, variable time delays, dynamics dependent on the hypnotic agent, model analysis variability, agent and stability issues. The modelling and automatic control of anaesthesia is believed to (i) benefit the safety of the patient undergoing surgery as side-effects may be reduced by optimizing the drug infusion rates, and (ii) support anaesthetists during critical situations by automating the drug delivery systems. In this work we have developed several advanced explicit/multi-parametric model predictive (mp-MPC) control strategies for the control of depth of anaesthesia. State estimation techniques are developed and used simultaneously with mp-MPC strategies to estimate the state of each individual patient, in an attempt to overcome the challenges of inter- and intra- patient variability, and deal with possible unmeasurable noisy outputs. Strategies to deal with the nonlinearity have been also developed including local linearization, exact linearization as well as a piece-wise linearization of the Hill curve leading to a hybrid formulation of the patient model and thereby the development of multiparametric hybrid model predictive control methodology. To deal with the inter- and intra- patient variability, as well as the noise on the process output, several robust techniques and a multiparametric moving horizon estimation technique have been design and implemented. All the studies described in the thesis are performed on clinical data for a set of 12 patients who underwent general anaesthesia.Open Acces

Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs' monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient's condition on the most suited device within the physician's reach

Into the Black Box: Designing for Transparency in Artificial Intelligence

Indiana University-Purdue University Indianapolis (IUPUI)The rapid infusion of artificial intelligence into everyday technologies means that consumers are likely to interact with intelligent systems that provide suggestions and recommendations on a daily basis in the very near future. While these technologies promise much, current issues in low transparency create high potential to confuse end-users, limiting the market viability of these technologies. While efforts are underway to make machine learning models more transparent, HCI currently lacks an understanding of how these model-generated explanations should best translate into the practicalities of system design. To address this gap, my research took a pragmatic approach to improving system transparency for end-users. Through a series of three studies, I investigated the need and value of transparency to end-users, and explored methods to improve system designs to accomplish greater transparency in intelligent systems offering recommendations. My research resulted in a summarized taxonomy that outlines a variety of motivations for why users ask questions of intelligent systems; useful for considering the type and category of information users might appreciate when interacting with AI-based recommendations. I also developed a categorization of explanation types, known as explanation vectors, that is organized into groups that correspond to user knowledge goals. Explanation vectors provide system designers options for delivering explanations of system processes beyond those of basic explainability. I developed a detailed user typology, which is a four-factor categorization of the predominant attitudes and opinion schemes of everyday users interacting with AI-based recommendations; useful to understand the range of user sentiment towards AI-based recommender features, and possibly useful for tailoring interface design by user type. Lastly, I developed and tested an evaluation method known as the System Transparency Evaluation Method (STEv), which allows for real-world systems and prototypes to be evaluated and improved through a low-cost query method. Results from this dissertation offer concrete direction to interaction designers as to how these results might manifest in the design of interfaces that are more transparent to end users. These studies provide a framework and methodology that is complementary to existing HCI evaluation methods, and lay the groundwork upon which other research into improving system transparency might build

Ohjelmistokehitys lämpötilakontrolloidun silmänpohjan epiteelisolujen lämmityslaitetta varten

Age-related macular degeneration (AMD) was the leading cause for unavoidable blindness in 2010 and continues to affect an estimated 150 million people worldwide. It has been suggested that heating the retinal pigment epithelium (RPE) could slow down the progress of the disease or even cure it entirely. The treatment consists of heating the retina of an eye to therapeutic temperatures to inflict the generation of heat-shock proteins (HSPs). A device that relies on electroretinogram (ERG) recordings while inflicting local hyperthermia on the RPE has been developed in our research team. The measured ERG responses can be characterised and shown a direct dependency to the experienced temperature at the retina. These in turn are utilised in controlling the heating of the retina to therapeutic temperatures. The aim of this thesis was to implement a new software for the use of such a device, while considering the needs this software must account for to facilitate a reliable, safe and useful software interaction for the RPE heating device. Eight distinct requirements for the new software were identified: maintainability; dynamicity; accuracy and precision; pulse sequences; automation; safety, error handling and user friendliness; testing and validation; as well as documentation. The software was implemented with National Instruments LabVIEW™ and MathWorks MATLAB®. The results were validated and verified with unit testing, bench testing and in a full experiment on a mouse subject. The bench testing and mouse experiment testing provided satisfying results. The software functioned without errors during both types of testing or only had very minute types of errors. The software could still be developed to contain more automation, such as factoring in safety features through eye movement detection and more importantly facilitating feedback-controlled heating through a PID controller, which would be of importance when planning clinical trials and use of the device in treatment of AMD.Silmänpohjan ikärappeuma (AMD) oli yleisin vailla parannuskeinoa oleva, sokeutta aiheuttava sairaus vuonna 2010, ja se vaikuttaa noin 150 miljoonan ihmisen elämään maailmanlaajuisesti. Kirjallisuudessa on esitetty, että silmänpohjan pigmenttiepiteelikerroksen (RPE) lämmittäminen voisi hidastaa taudin kulkua tai parantaa sen kokonaan. Tällainen hoito saavutettaisiin lämmittämällä silmänpohjaa terapeuttisiin lämpötiloihin, jolloin saadaan aikaan lämpösokkiproteiinien (HSP) muodostumista. Tutkimusryhmässämme on kehitetty laite, joka perustuu elektroretinogrammin (ERG) rekisteröintiin, samalla kun lämmityslaserilla aiheutetaan RPE:lle paikallinen hypertermia. Talteenotetulla ERG:llä voidaan estimoida silmänpohjan lämpötilaa. Estimoitua lämpötilaa hyödynnetään lämmityslaserin säätämisessä terapeuttiselle lämpötila-alueelle. Tämän diplomityön tavoitteena oli implementoida uusi ohjelmisto kyseistä laitetta varten, samalla ottaen huomioon luotettavuus- ja turvallisuusnäkökulmia, sekä muita hyödyllisiä ominaisuuksia laitteen ja ohjelmiston yhteistoiminnassa. Työssä määriteltiin kahdeksan eri vaatimusta uudelle ohjelmistolle: ylläpidettävyys; dynaamisuus; tarkkuus ja täsmällisyys; pulssisekvenssit; automaatio; turvallisuus, virheiden käsittely ja käyttäjäystävällisyys; verifiointi ja validointi; sekä dokumentointi. Ohjelmisto toteutettiin käyttäen ohjelmistoja: National Instruments LabVIEW™ sekä MathWorks MATLAB®. Ohjelmisto validoitiin testaamalla kaikki osiot erikseen (yksikkötestaus) sekä koko ohjelmisto mittausta simuloivassa tilanteessa. Lopuksi ohjelmisto testattiin myös oikeassa hiiren silmänpohjan lämmityskokeessa. Testauksissa ohjelmisto toimi halutulla tavalla ja esiintyneet virheet pystyttiin nopeasti korjaamaan viimeistä versiota varten. Ohjelmistoa voidaan jatkossa kehittää sisältämään enemmän automaatiota, kuten turvallisuusominaisuuksia silmän liikkeiden tunnistamiseen sekä lämmityksen säätämiseen takaisinkytkentämenetelmällä. Molemmat olisivat tärkeitä ominaisuuksia siirryttäessä kliiniseen tutkimukseen ja laitteen kliiniseen käyttöön AMD:n hoitamiseksi

Virtual Assisted Self Interviewing (VASI): An expansion of survey data collection methods to the virtual worlds by means of VDCI

Changes in communication technology have allowed for the expansion of data collection modes in survey research. The proliferation of the computer has allowed the creation of web and computer assisted auto-interview data collection modes. Virtual worlds are a new application of computer technology that once again expands the data collection modes by VASI (Virtual Assisted Self Interviewing). The Virtual Data Collection Interface (VDCI) developed at Indiana University in collaboration with the German Socio-Economic Panel Study (SOEP) allows survey researchers access to the population of virtual worlds in fully immersive Heads-up Display (HUD)-based survey instruments. This expansion needs careful consideration for its applicability to the researcher's question but offers a high level of data integrity and expanded survey availability and automation. Current open questions of the VASI method are an optimal sampling frame and sampling procedures within e. g. a virtual world like Second Life (SL). Further multimodal studies are proposed to aid in evaluating the VDCI and placing it in context of other data collection modes