Browsing by Author "Otieno, Christopher Oyuech"
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Item A Decision Support Model for Predicting Avoidable Re-Hospitalization of Breast Cancer Patients in Kenyatta National Hospital(Journal of Software Engineering and Applications, 2022) Otieno, Christopher Oyuech; Oboko, Robert Obwocha; Kahonge, Andrew MwauraThis study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision- making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support mod-el that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.Item Towards an Effective Communication in the Care of Patients with Long Term Disease in Kenya via Cybernetic—A Systematic Review(Open Journal of Applied Sciences, vol. 13, issue 11, pp. 2094-2126, 2023) Otieno, Christopher Oyuech; Makara, Martha W. Kiarie; James, Naomi N.; Liyai, Grace MbogoIn this paper, to effectively treat chronic disorders and improve the standard of care, effective communication between patients and healthcare professionals was essential. The aim of the study was to review the literature on how good communication might improve treatment outcomes for Kenyan patients with chronic and terminal illnesses and to determine whether Cybernetic electronic communication can improve those outcomes even more. We uncovered the history of treatment outcomes for chronic and terminal diseases in this research study, both with and without communication at the core of the patient’s care plan. We discussed the importance of good communication in the treatment of patients with chronic and terminal illnesses and why it is a momentous endeavor comparable to medical diagnosis and treatment for the long-term health of patients. To locate pertinent material for the background literature study, we carried out a comprehensive literature search. Although the preliminary literature review was a continuation of the introduction research, it also highlighted the paucity of local Kenyan literature and suggested that improved communication might help patients with chronic and terminal illnesses have better treatment outcome. Methodology maintained the literature search, as a systematic literature review focused on core of the study, making separate sections of the same body necessary. This ensured that a methodological literature search section is as comprehensive as possible. We used an integrated PRISM model to limit a comprehensive literature search and a systematic literature review design as part of the overall process. Non-probability sampling and snowball approaches on literary papers over the previous 17 years were used in this arrangement. Since this was a multidisciplinary study, the four experts who also serve as authors were chosen from within their respective fields of expertise to design the study. They created search strategies, generated key words, looked up keywords in database engines, assessed the results of the literature using the PRISMA logical model, looked over successful literature, and triangulated their findings. The conclusions of the experts individually revealed a convergence of thoughts, beliefs, and practices across. The study concluded that even though there isn’t much research done in Kenya on the same subject; what is available illustrates how crucial good communication is for patients with chronic illnesses. The study’s findings also highlighted the positive effects of effective communication between patients and healthcare professionals on treatment plan adherence, patient satisfaction, and overall health outcomes. The results also noted that in order to improve patient care and outcome, Kenyan healthcare workers should underscore developing their communication skills. The study also found that the incorporation of cybernetics is crucial if a truly effective communication is required so as to enable centered care for patients with long-term diseases in Kenya. The goal of the Cybernetics is to activate genuinely effective communication in the care of Patients with long-term disease in Kenya. This study is organized to begin with an abstract, followed by keywords, an introduction, literature review, methodology, findings, discussion, and finally conclusions.