An Investigation of Registered Nurses’ Knowledge and Decision-Making Processes In Relation to the Management of Adults With Diabetic Ketoacidosis

Introduction: Diabetic ketoacidosis (DKA) is an acute complication of diabetes. Registered nurses (RNs) knowledge with regard to DKA has never been investigated in any depth, nor has their decision making ever been examined in this specific context. Research Significance: Nursing research literature acknowledges that nurses have an important role in the management of patient with DKA. However, there is very little empirical evidence available to support this claim. The purpose of this study is to provide evidence of the level of knowledge, the decision-making processes and the factors that influence nurses’ decision making whilst managing patients with DKA. Methodology: A sequential mixed methods design in four phases was used. An online survey was developed and tested for clarity, internal consistency, content validity and reliability. The survey was administered to nurses who were likely to have been involved in the care of patients with DKA in their clinical practice and the data analysed using descriptive statistics. Semi-structured interviews were then conducted based on the results of the survey. Finally, both data sets were amalgamated in a mixed methods analysis to develop recommendations for future research and clinical practice. Results: The survey results indicate that RNs had varying levels of knowledge in relation to DKA and strengths and weaknesses at different stages of their decision-making process. Some of the knowledge deficits related to policy, pharmacology and psychosocial issues. Four themes emerged from the qualitative data relating to the factors that influence RNs decision making whilst managing patients with DKA including policy, staffing, patients and confidence. The mixed methods analysis resulted in a number of recommendations for xix organisational and education strategies to enables RNs to provide holistic and evidence-based care to patients with DKA. Conclusion: This study found that RNs were generally able to demonstrate adequate levels of knowledge to manage patients with DKA and utilised all the stages of the Clinical Judgment Model when making decisions. It was found that the most significant knowledge gaps related to information directly out of the hospital policy, which nurses found served as both an enabler and a barrier to their decision making. There were a number of limitations in this study, many relating to the COVID-19 pandemic. The participants requested a flow chart be developed to aid their knowledge application and decision-making practices whilst managing these acutely unwell and complex patients

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Using conceptual graphs for clinical guidelines representation and knowledge visualization

The intrinsic complexity of the medical domain requires the building of some tools to assist the clinician and improve the patient’s health care. Clinical practice guidelines and protocols (CGPs) are documents with the aim of guiding decisions and criteria in specific areas of healthcare and they have been represented using several languages, but these are difficult to understand without a formal background. This paper uses conceptual graph formalism to represent CGPs. The originality here is the use of a graph-based approach in which reasoning is based on graph-theory operations to support sound logical reasoning in a visual manner. It allows users to have a maximal understanding and control over each step of the knowledge reasoning process in the CGPs exploitation. The application example concentrates on a protocol for the management of adult patients with hyperosmolar hyperglycemic state in the Intensive Care Unit

Risk Factors of Intraoperative Dysglycemia in Elderly Surgical Patients

BACKGROUNDː Dysglycemia is associated with adverse outcome including increased morbidity and mortality in surgical patients. Acute insulin resistance due to the surgical stress response is seen as a major cause of so-called stress hyperglycemia. However, understanding of factors determining blood glucose (BG) during surgery is limited. Therefore, we investigated risk factors contributing to intraoperative dysglycemia. METHODSː In this subgroup investigation of the BIOCOG study, we analyzed 87 patients of ≥ 65 years with tight intraoperative BG measurement every 20 min during elective surgery. Dysglycemia was defined as at least one intraoperative BG measurement outside the recommended target range of 80-150 mg/dL. Additionally, all postoperative BG measurements in the ICU were obtained. Multivariable logistic regression analysis adjusted for age, sex, American Society of Anesthesiologists (ASA) status, diabetes, type and duration of surgery, minimum Hemoglobin (Hb) and mean intraoperative norepinephrine use was performed to identify risk factors of intraoperative dysglycemia. RESULTSː 46 (52.9%) out of 87 patients developed intraoperative dysglycemia. 31.8% of all intraoperative BG measurements were detected outside the target range. Diabetes [OR 9.263 (95% CI 2.492, 34.433); p=0.001] and duration of surgery [OR 1.005 (1.000, 1.010); p=0.036] were independently associated with the development of intraoperative dysglycemia. Patients who experienced intraoperative dysglycemia had significantly elevated postoperative mean (p<0.001) and maximum BG levels (p=0.001). Length of ICU (p=0.007) as well as hospital stay (p=0.012) were longer in patients with dysglycemia. CONCLUSIONSː Diabetes and duration of surgery were confirmed as independent risk factors for intraoperative dysglycemia, which was associated with adverse outcome. These patients, therefore, might require intensified glycemic control. Increased awareness and management of intraoperative dysglycemia is warranted

Evaluation of a Comprehensive Diabetes Mellitus Protocol at a Rural, Federally Qualified Health Center in Southern West Virginia

Background: Diabetes mellitus is a chronic disease that affects nearly 34 million Americans. In rural Appalachia, the population is affected disproportionately at a rate of 14% compared to the national average of 10%. Diabetes is a lifelong, chronic condition managed best by a multidisciplinary team-based approach to achieve optimal disease control. Best practices in the care of diabetes support the use of evidenced based care protocols and leveraging technology to decrease the burden of disease. Type 2 diabetes mellitus (T2DM) is the most common type, making it the focal population for evaluation. Purpose: The purpose of this project was to evaluate the impact of a standardized diabetes mellitus protocol for patients with T2DM at a rural federally qualified health center (FQHC) in rural southern West Virginia. Program evaluation completes the care cycle. This information can inform stakeholders about a protocol’s effectiveness, thus leading to recommendations for change to improve T2DM education and outcomes in healthcare delivery. Intervention and Methods: Program Evaluation was completed using a retrospective chart review and a provider survey. Objective 1 was to evaluate the diabetes protocol using seven core quality measures (hemoglobin A1c, blood pressure, low density lipoprotein [LDL] cholesterol, diabetes self-management education (DSME), annual urine microalbumin, retinopathy, and neuropathy exams) over three years (pre-protocol T1 and post-protocol T2 and T3). Objective 2 utilized a provider survey to determine behaviors regarding Type 2 Diabetes Mellitus (T2DM) protocol and diabetes education team awareness and utilization. Results: Results for Objective 1 found statistically significant improvement at T3 for diastolic blood pressure and annual microalbumin, but not for other metrics. Overall, most metrics noted improvement or stabilization over all time periods despite the evaluation taking place during the COVID-19 pandemic. Results for Objective 2 found that majority of providers were aware of the T2DM protocol and utilized the diabetes education accreditation program (DEAP) team regularly. Conclusion: The evaluation provided valuable insight on the current efforts to reduce the burden of diabetes mellitus at the facility in rural West Virginia. Over half of all core quality measures met facility benchmarks, however measures for DSME referral, A1c, retinopathy and neuropathy exams are still lower than expected. All providers agree that COVID-19 had a negative impact on patient care. Recommendations for improvements in practice include a patient-individualized approach to care with increasing utilization of the DEAP team, and continuous provider support of DSME in the management of patients with T2DM

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces