OBJECTIVES: Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers.
METHODS: The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed.
RESULTS: The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking.
CONCLUSIONS: This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.
DESIGN/METHODOLOGY/APPROACH: A literature review was performed on issues, sources, management and approaches to HISs-induced errors. A critical review of selected models was performed in order to identify medical error dimensions and elements based on human, process, technology and organisation factors.
FINDINGS: Various error classifications have resulted in the difficulty to understand the overall error incidents. Most classifications are based on clinical processes and settings. Medical errors are attributed to human, process, technology and organisation factors that influenced and need to be aligned with each other. Although most medical errors are caused by humans, they also originate from other latent factors such as poor system design and training. Existing evaluation models emphasise different aspects of medical errors and could be combined into a comprehensive evaluation model.
RESEARCH LIMITATIONS/IMPLICATIONS: Overview of the issues and discourses in HIS-induced errors could divulge its complexity and enable its causal analysis.
PRACTICAL IMPLICATIONS: This paper helps in understanding various types of HIS-induced errors and promising prevention and management approaches that call for further studies and improvement leading to good practices that help prevent medical errors.
ORIGINALITY/VALUE: Classification of HIS-induced errors and its management, which incorporates a socio-technical and multi-disciplinary approach, could guide researchers and practitioners to conduct a holistic and systematic evaluation.
OBJECTIVES: To evaluate the economic burden of treating cancer patients.
METHOD: Descriptive cross-sectional cost of illness study in the leading teaching and referral hospital in Kenya, with data collected from the hospital files of sampled adult patients for treatment during 2016.
RESULTS: In total, 412 patient files were reviewed, of which 63.4% (n = 261) were female and 36.6% (n = 151) male. The cost of cancer care is highly dependent on the modality. Most reviewed patients had surgery, chemotherapy and palliative care. The cost of cancer therapy varied with the type of cancer. Patients on chemotherapy alone cost an average of KES 138,207 (USD 1364.3); while those treated with surgery cost an average of KES 128,207 (1265.6), and those on radiotherapy KES 119,036 (1175.1). Some patients had a combination of all three, costing, on average, KES 333,462 (3291.8) per patient during the year.
CONCLUSION: The cost of cancer treatment in Kenya depends on the type of cancer, the modality, cost of medicines and the type of inpatient admission. The greatest contributors are currently the cost of medicines and inpatient admissions. This pilot study can inform future initiatives among the government as well as private and public insurance companies to increase available resources, and better allocate available resources, to more effectively treat patients with cancer in Kenya. The authors will be monitoring developments and conducting further research.
Objective: This study aimed to develop a maternal blues scale through bonding attachments to predict postpartum blues.
Method: The research design consisted of three stages: 1) phenomenology design and focus group discussion; 2) development and construction of the maternal blues scale, and 3) a cross-sectional study to measure validation of the scales. Respondents were postpartum mothers in the first week after birth. The sample comprised 501 participants. Sampling was done by consecutive sampling at the Public Health Center (PUSKESMAS) in the South Jakarta area. Data analysis used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), correlation, and a diagnostic testing .
Results: Item analysis produced 32 items consisting of 24 items regarding the mother's role and duties as internal factors and eight factors involving social, cultural, and economic support as external factors. Both factors were valid and reliable in predicting postpartum blues with indicators (t loading factors ≥ 1.96, standardized loading factor (SLF) ≥.50, internal factors: construct reliability (CR) ≥ .70 and extraction variants (VE) ≥ .50 and external factors: CR ≥ .74 to .83 VE ≥ .50 to .63). The relationship with Kennerley's maternity blues as a gold standard was significant. Internal factors had a score of 53, with a sensitivity of 60.2%. The external factors score was 19, with a sensitivity of 77.3%.
Conclusion: The new scale for postpartum blues prediction developed displayed internal consistency and validity of each indicator (internal and external factors) that was good (CR ≥ .70; VE ≥ .50). This scale provides a feasible tool to predict postpartum blues.