Using informatics to improve healthcare quality

Otokiti, Ahmed

doi:10.1108/ijhcqa-03-2018-0062

Cited by 17 publications

(12 citation statements)

References 8 publications

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“…Furthermore, for sure our results confirm that a qualitative cognitive tool, as the Kahneman et al (2011) checklists, could potentially help physicians avoid these errors, but it needs to be integrated in daily practice, also as a more usable electronic version (Raymond et al 2017;Otokiti 2019). For sure this manuscript presents some limitations; most of them are those related to the use of the qualitative method which implies the interpretive role of researchers and limited extension of data.…”

Section: Discussionmentioning

confidence: 52%

Enhancing Healthcare Decision-Making Process: Findings from Orthopaedic Field

Schettini

Palozzi

Chirico

2020

Administrative Sciences

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In the healthcare field, the decision-making process is part of the broad spectrum of “clinical reasoning”, which is recognised as the whole process by which a physician decides about patients’ treatments and cares. Several clinicians’ intrinsic variables lead to this decisional path. Little is known about the inference of these variables in triggering biases in decisions about the post-discharge period in the surgical field. Accordingly, this research aims to understand if and how cognitive biases can affect orthopaedists in decision-making regarding the follow-up after knee and hip arthroplasty. To achieve this goal, an interview-based explorative case study was run. Three key-decisional orthopaedic surgeons were interviewed through a quality control tool aimed at monitoring the causes and effects of cognitive distortions. Coherently with the literature, eight biases come to light. All the interviewees agree on the presence of four common biases in orthopaedic surgery (Affect heuristic, Anchoring, Halo effect, Saliency). The other biases (Groupthink, Availability, Overconfidence, Confirmation), instead, depending on specific physicians’ intrinsic variables; namely: (i) working experience; (ii) working context. This finding contributes to the debate about the application of cognitive tools as leverage for improving the quality of clinical decision-making process and, indirectly, enhancing better healthcare outcomes.

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Section: Discussionmentioning

confidence: 52%

Enhancing Healthcare Decision-Making Process: Findings from Orthopaedic Field

Schettini

Palozzi

Chirico

2020

Administrative Sciences

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“…Diferentes estudios demostraron que el uso del algoritmo de IA predijo con precisión cuando y situación el paciente fue trasladado del SEM a cuidados críticos en la unidad hospitalaria 21,22,23 . Actualmente en medicina, los procesos de toma de decisiones se basan en la disponibilidad de evidencia objetiva y confiable, la investigación, así como la adecuada interpretación de los hechos disponibles con la incorporación de las relaciones riesgo-beneficio del paciente en cada paso de decisión; sin embargo, la práctica de la medicina en el mundo real nos ha enseñado que la evidencia no siempre está disponible, la penetración del conocimiento lleva tiempo y las decisiones sobre pacientes individuales pueden no siempre ser objetivas 24 . La mayoría de los cardiólogos de hoy en día tienen más probabilidades de asociar el término IA con un fenómeno de ciencia ficción aún distante, pero la realidad es que estás a punto de conquistar la medicina, incluida la medicina cardiovascular.…”

Section: Inteligencia Artificial En La Decisión Médicaunclassified

La inteligencia artificial en la educación médica y la predicción en salud

Joison¹,

Barcudi²,

Majul³

et al. 2021

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La inteligencia artificial tiene el potencial de transformar la forma en que se brinda la atención médica. Puede respaldar mejoras en los resultados y aumentar la productividad y la eficiencia de la prestación de los servicios. En servicios de las diferentes especialidades los avances realizados a nivel hardware deben desarrollarse en paralelo con los métodos de aprendizaje automático, aspectos que la inteligencia artificial contribuye para promover un cambio de paradigma significativo en las más diversas áreas de la medicina. Es importante en la educación médica como eje para el conocimiento y en la toma de decisiones que pueden mejorar el desempeño de los profesionales. Los estudiantes de medicina de nueva generación pueden adaptarse perfectamente a los nuevos métodos digitalizados en un contexto médico globalizado, incluida la inteligencia artificial. Por ello es importante tener como objetivos a implementar en los planes de estudio e introducir programas educativos representativos de esta tecnología. Es fundamental que todas las áreas del Sistema de Salud tengan confianza en los sistemas informáticos específicamente en el aprendizaje profundo, no solo por la información concreta y objetiva que de él se deriva sino también por la posibilidad de predecir eventos futuros, brindando alta certeza en cuanto al diagnóstico y tratamiento de enfermedades.

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“…A number of HIS technologies have been deployed in the healthcare sector, such as Electronic health (e-health), Medical Internet of Things (MIoT), Internet of Medical Things (IoMT, Internet of Healthcare Things (IoHT), and more recently, the blockchain (BC) enabled HISs. As poined out in [17] , electronic medical records (EMR) system usability is a major healthcare informatics issue owing to lack of standard user interfaces. In addition, patient harm as a result of usability errors and user-unfriendly functionalities.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Information Systems Success Model for Patient Information Assurance

Agunga

Agola

Abuonji

2021

j. of Comput. sci. res.

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The current health information systems have many challenges such as lack of standard user interfaces, data security and privacy issues, inability to uniquely identify patients across multiple hospital information systems, probable misuse of patient data, high technological costs, resistance to technology deployments in hospital management, lack of data gathering, processing and analysis standardization. All these challenges, among others hamper either the acceptance of the health information systems, operational efficiency or expose patient information to cyber attacks. In this paper, an enhanced information systems success model for patient information assurance is developed using an amalgamation of Technology Acceptance Model (TAM) and Information Systems Success Model (ISS). This involved the usage of Linear Structured Relationship (LISREL) software to model a combination of ISS and Intention to Use (ITU), TAM and ITU, ISS and user satisfaction (US), and finally TAM and US. The sample size of 110 respondents was obtained based on the total population of 221 using the Conhrans formula. Thereafter, simple random sampling was employed to select members within each category of employees to take part in the study. The questionnaire as a research tool was checked for reliability via Cronbach’s Alpha. The results obtained showed that for ISS and ITU modeling, only perceived ease of use, system features, response time, flexibility, timeliness, accuracy, responsiveness and user training positively influenced the intention to use. However, for the TAM and ITU modeling, only TAM’s measures such as timely information, efficiency, increased transparency, and proper patient identification had a positive effect on intension to use. The ISS and US modeling revealed that perceived ease of use had the greatest impact on user satisfaction while response time had the least effect on user satisfaction. On its part, the TAM and US modeling showed that timely information, effectiveness, consistency, enhanced communication, and proper patients identification had a positive influence on user satisfaction.

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Using informatics to improve healthcare quality

Cited by 17 publications

References 8 publications

Enhancing Healthcare Decision-Making Process: Findings from Orthopaedic Field

Enhancing Healthcare Decision-Making Process: Findings from Orthopaedic Field

La inteligencia artificial en la educación médica y la predicción en salud

Enhanced Information Systems Success Model for Patient Information Assurance

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