scholarly journals Health Information Resources Accessibility as Predictors for Clinical Decision Making among Medical Doctors in Obafemi Awolowo University Ile-Ife, Nigeria

2021 ◽  
Vol 11 (4) ◽  
pp. 81-91
Author(s):  
Moyosore O. Adegboye ◽  
Samuel Adeyoyin
Keyword(s):  
Decision Making ◽  
Focus Group ◽  
Health Information ◽  
Clinical Decision Making ◽  
Group Discussion ◽  
Clinical Decision ◽  
Information Resources ◽  
Medical Doctors ◽  
Core Skills ◽  
Health Information Resources

The study evaluates health information resources as predictors for clinical decision- making among medical doctors in Obafemi Awolowo University Teaching Hospital Ile-Ife. A survey research design was adopted by the study and random sampling technique was used to select 265 medical doctors from a population of 822. Primary data were obtained on socioeconomic characteristics of the respondents, level of accessibility, frequency and various core skills of health information resources using a structured questionnaire and focus group discussion (FGD). Data were analyzed using frequency counts, percentage and mean. Results revealed that 59.8% of the respondents were male while 51.1% were female. Findings, however, showed that pattern recognition from experience ( &#x0304 = 3.32), critical thinking without emotion (&#x0304 = 3.16), hypothesis updating (&#x0304 = 3.607) and perception based confidence (&#x0304 = 2.97) were the core skills used by the medical doctors in clinical decision making. The focus group discussion emphasized that medical doctors should possess critical thinking without emotions and good time pressure balance in order to make accurate clinical decisions. The study concludes that medical doctors have quality access to health information resources to make clinical decisions. The study, therefore recommended regular trainings of medical personnel on health information resources to ensure accurate and sound decision making in order to enhance optimal performance. Keywords Information sharing, Job satisfaction, Librarians, Private Universities

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

scholarly journals Exploring the role of the Care and Health Information Exchange (CHIE) in clinical decision-making: a realist evaluation

2020 ◽  
Author(s):  
Philip Scott ◽  
Elisavet Andrikopoulou ◽  
Haythem Nakkas ◽  
Paul Roderick
Keyword(s):  
Decision Making ◽  
Health Information ◽  
Information Exchange ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Social Care ◽  
Health Information Exchange ◽  
Clinical Decision ◽  
Health And Social Care ◽  
The Impact

Background: The overall evidence for the impact of electronic information systems on cost, quality and safety of healthcare remains contested. Whilst it seems intuitively obvious that having more data about a patient will improve care, the mechanisms by which information availability is translated into better decision-making are not well understood. Furthermore, there is the risk of data overload creating a negative outcome. There are situations where a key information summary can be more useful than a rich record. The Care and Health Information Exchange (CHIE) is a shared electronic health record for Hampshire and the Isle of Wight that combines key information from hospital, general practice, community care and social services. Its purpose is to provide clinical and care professionals with complete, accurate and up-to-date information when caring for patients. CHIE is used by GP out-of-hours services, acute hospital doctors, ambulance service, GPs and others in caring for patients. Research questions: The fundamental question was How does awareness of CHIE or usage of CHIE affect clinical decision-making? The secondary questions were What are the latent benefits of CHIE in frontline NHS operations? and What is the potential of CHIE to have an impact on major NHS cost pressures? The NHS funders decided to focus on acute medical inpatient admissions as the initial scope, given the high costs associated with hospital stays and the patient complexities (and therefore information requirements) often associated with unscheduled admissions. Methods: Semi-structured interviews with healthcare professionals to explore their experience about the utility of CHIE in their clinical scenario, whether and how it has affected their decision-making practices and the barriers and facilitators for their use of CHIE. The Framework Method was used for qualitative analysis, supported by the software tool Atlas.ti. Results: 21 healthcare professionals were interviewed. Three main functions were identified as useful: extensive medication prescribing history, information sharing between primary, secondary and social care and access to laboratory test results. We inferred two positive cognitive mechanisms: knowledge confidence and collaboration assurance, and three negative ones: consent anxiety, search anxiety and data mistrust. Conclusions: CHIE gives clinicians the bigger picture to understand the patient's health and social care history and circumstances so as to make confident and informed decisions. CHIE is very beneficial for medicines reconciliation on admission, especially for patients that are unable to speak or act for themselves or who cannot remember their precise medication or allergies. We found no clear evidence that CHIE has a significant impact on admission or discharge decisions. We propose the use of recommender systems to help clinicians navigate such large volumes of patient data, which will only grow as additional data is collected.

scholarly journals Enhancing Student Knowledge of Diabetes through Virtual Choose Your Own Adventure Patient Case Format

Pharmacy ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 87
Author(s):  
Tyler Marie Kiles ◽  
Elizabeth A. Hall ◽  
Devin Scott ◽  
Alina Cernasev
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Diabetes Management ◽  
Group Discussion ◽  
Clinical Decision ◽  
Pharmacy Students ◽  
Patient Case ◽  
Student Knowledge ◽  
Innovative Teaching ◽  
Significant Difference

Educational strategies to teach pharmacy students about diabetes are necessary to prepare future pharmacists to manage complex patients. The Choose Your Own Adventure (CYOA) patient case format is an innovative activity that presents a patient case in an engaging way. The objectives of this study were (1) to describe the development of the innovative teaching activity and (2) to assess its effect on student knowledge and confidence in outpatient management of diabetes. The CYOA patient case activity was designed by transforming a traditional paper patient case involving outpatient diabetes management into an interactive format utilizing an online platform. The activity was conducted with 186 second-year pharmacy students in a skills-based course. This activity was administered virtually through a combination of small group work and large group discussion. After completion of the activity, students completed an online self-assessment questionnaire. Of 178 completed questionnaires, there was a statistically significant difference in students’ self-ratings after versus before the activity for all survey items (p < 0.001). The CYOA activity improved self-reported knowledge of outpatient diabetes management and increased self-reported confidence in clinical decision-making skills. This format shows promise as an educational tool that may be adapted for other disease states to enhance clinical decision-making skills.

DISCUSSION AGENDA FOR THE SESSION ON MEDICAL DECISION MAKING and MINUTES OF A GROUP DISCUSSION ON CLINICAL DECISION MAKING

1988 ◽  
pp. 599-612
Author(s):  
Milton C. Weinstein ◽  
Harvey V. Fineberg ◽  
Barbara J. McNeil ◽  
Stephen G. Pauker ◽  
Robert J. Quinn
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Clinical Decision Making ◽  
Group Discussion ◽  
Clinical Decision ◽  
Medical Decision

scholarly journals Health Information Use by Resident Doctors in Lagos State University Teaching Hospital, Ikeja, Lagos State, Nigeria

2020 ◽  
Vol 11 (3) ◽  
pp. 41-50
Author(s):  
Maria Ehioghae ◽  
Ezinwanyi Madukoma
Keyword(s):  
Decision Making ◽  
Health Information ◽  
Health Systems ◽  
Teaching Hospital ◽  
Clinical Decision Making ◽  
Information Use ◽  
Clinical Decision ◽  
University Teaching ◽  
State University ◽  
Lagos State

The study interrogates health information use by resident doctors in Lagos State University Teaching hospital (LASUTH), Lagos State. Health information has been variously described as the “foundation” for better health, as the “glue” holding the health systems together and as the “oil” keeping the health systems running. It is important for making the right clinical decisions and enhancing professionalism. A survey research design was adopted by the study and the enumeration technique was used to cover all 115 resident doctors that constituted the population. Out of the 115 questionnaire copies administered, 94 copies were returned for data analysis, making the response rate to be 81.7%. The data collected were analyzed using frequency counts and percentages. Findings revealed that the majority of resident doctors in LASUTH have access and use, to a large extent, health information for clinical decision-making. It is, however, recommended that to improve on health information sharing, workshops and seminars on health information should be regularly conducted for resident doctors in LASUTH. This, expectedly, will expose them to new health information trends that will enhance their clinical experience. Keywords: Health Information, Information Use, Resident Doctors, Clinical Decision-making

scholarly journals Explaining Deep Neural Networks for Knowledge Discovery in Electrocardiogram Analysis

2021 ◽  
Author(s):  
Steven Hicks ◽  
Jonas Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Network ◽  
Deep Learning Model

Deep learning-based tools may annotate and interpret medical tests more quickly, consistently, and accurately than medical doctors. However, as medical doctors remain ultimately responsible for clinical decision-making, any deep learning-based prediction must necessarily be accompanied by an explanation that can be interpreted by a human. In this study, we present an approach, called ECGradCAM, which uses attention maps to explain the reasoning behind AI decision-making and how interpreting these explanations can be used to discover new medical knowledge. Attention maps are visualizations of how a deep learning network makes, which may be used in the clinic to aid diagnosis, and in research to identify novel features and characteristics of diagnostic medical tests. Here, we showcase the use of ECGradCAM attention maps using a novel deep learning model capable of measuring both amplitudes and intervals in 12-lead electrocardiograms.

Decision Support

2019 ◽  
pp. 180-199
Author(s):  
Christoph U. Lehmann ◽  
Karl E. Misulis ◽  
Mark E. Frisse
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Medical Errors ◽  
Clinical Decision Making ◽  
Adverse Drug Events ◽  
Clinical Decision ◽  
Information Resources ◽  
Clinical Practices

Decision support is a broad technique that seeks to bring information to bear at the time a clinician is taking actions that are driven by other data. Clinical decision-making methodology depends on the complexity of the patient’s case, the certainty of a diagnosis, available treatment and diagnostic resources, reliability of information resources, training of the clinician, and psychological makeup of the clinician. Most clinical decision support efforts seek to improve workflow, enforce best clinical practices, or mitigate adverse drug events. Clinical decision support can reduce medical errors, improve nutrition, prevent orders on the wrong patient, and reduce costs. Clinical and administrative decision support can lead to more effective outcomes, improved quality, and lower costs.

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