The function of competitive intelligence in South African insurance post-COVID-19 outbreak

Background: Competitive intelligence (CI) involves monitoring competitors and providing organizations with actionable and meaningful intelligence. Some studies have focused on the role of CI in other industries post-COVID-19 pandemic. Objectives: This article aims to examine the impact of COVID-19 on the South African insurance sector and how the integration of CI and related technologies can sustain the South African insurance sector post-COVID-19 epidemic. Method: Qualitative research with an exploratory-driven approach was used to examine the impact of the COVID-19 pandemic on the South African insurance sector. Qualitative secondary data analyses were conducted to measure insurance claims and death benefits paid during the COVID-19 pandemic. Results: The research findings showed that the COVID-19 pandemic significantly impacted the South African insurance industry, leading to a reassessment of pricing, products, and risk management. COVID-19 caused disparities in death benefits and claims between provinces; not everyone was insured. Despite challenges, South African insurers remained well-capitalised and attentive to policyholders. Integrating CI and analytical technologies could enhance the flexibility of prevention, risk management, and product design. Conclusion: COVID-19 requires digital transformation and CI for South African insurers' competitiveness. Integrating artificial intelligence (AI), big data (BD), and CI enhances value, efficiency, and risk assessments. Contribution: This study highlights the importance of integrating CI strategies and related technologies into South African insurance firms' operations to aid in their recovery from the COVID-19 crisis. It addresses a research gap and adds to academic knowledge in this area.

Retinitis pigmentosa in Usher syndrome in India: Electronic medical records driven big data analytics: Report III

Purpose: To describe the clinical presentation and demographic distribution of retinitis pigmentosa (RP) in patients with Usher syndrome (USH). Methods: This is a cross?sectional observational hospital?based study including patients presenting between March 2012 and October 2020. In total, 401 patients with a clinical diagnosis of USH and RP in at least one eye were included as cases. The data were retrieved from the electronic medical record database. For better analysis, all 401 patients were reclassified into three subtypes (type 1, type 2, and type 3) based on the USH criteria. Results: In total, there were 401 patients with USH and RP, with a hospital?based prevalence rate of 0.02% or 2/10,000 population. Further, 353/401 patients were subclassified, with 121 patients in type 1, 146 patients in type 2, and 86 patients in the type 3 USH group. The median age at presentation was 27 years (IQR: 17.5–38) years. There were 246 (61.35%) males and 155 (38.65%) females. Males were more commonly affected in all three subtypes. Defective night vision was the predominant presenting feature in all types of USH (type 1: 43 (35.54%), type 2: 68 (46.58%), and type 3: 40 (46.51%) followed by defective peripheral vision. Patients with type 2 USH had more eyes with severe visual impairment. Conclusion: RP in USH is commonly bilateral and predominantly affects males in all subtypes. Patients with USH and RP will have more affection of peripheral vision than central vision. The key message of our study is early visual and hearing rehabilitation in USH patients with prompt referral to otolaryngologists from ophthalmologists and vice versa.

Retinitis pigmentosa in Laurence-Moon-Bardet-Biedl syndrome in India: Electronic medical records driven big data analytics: Report II

Purpose: To describe the clinical presentation and demographic distribution of retinitis pigmentosa (RP) in Laurence–Moon–Bardet–Biedl (LMBB) syndrome patients. Methods: This is a cross?sectional observational hospital?based study wherein 244 patients with RP in LMBB syndrome presenting to our hospital network between March 2012 and October 2020 were included. An electronic medical record database was used for data retrieval. Results: There were 244 patients in total, with a hospital?based prevalence rate of 0.010% or 1000/100,000 population. The mean and median age of patients was 15.22 ± 7.56 and 14 (IQR: 10–18.5) years, respectively, with the majority being in the age group of 11–20 years (133/244 patients; 54.50%). Males were more commonly affected (164 patients; 67.21%), and the majority (182 patients; 74.59%) were students. All 244 patients (100%) complained of defective central vision at presentation. More than one?fourth of the patients had severe visual impairment to blindness at presentation. Prominent retinal feature at presentation was diffuse or widespread retinal pigment epithelial degeneration in all patients. Conclusion: Patients with RP in LMBB syndrome present mainly in the first to second decade of life with severe visual acuity impairment to blindness early in life. It is important to rule out LMBB syndrome in early?onset RP with central visual acuity impairment. On the contrary, all patients diagnosed or suspected with LMBB syndrome systemic features at physician clinic should also be referred for ophthalmic evaluation, low vision assessment, rehabilitation, and vice versa

The Role of Big Data Analytics in Digital Health for COVID-19 Prevention and Control in Asia

@#Big data analytics (BDA) in digital health is critical for gaining the knowledge needed to make decisions, with Asia at the forefront of utilising this technology for the Coronavirus disease 2019 (COVID-19). This review aims to study how BDA was incorporated into digital health in managing the COVID-19 pandemic in six selected Asian countries, discuss its advantages and barriers and recommend measures to improve its adoption. A narrative review was conducted. Online databases were searched to identify all relevant literature on the roles of BDA in digital health for COVID-19 preventive and control measures. The findings showed that these countries had used BDA for contact tracing, quarantine compliance, outbreak prediction, supply rationing, movement control, information update, and symptom monitoring. Compared to conventional approaches, BDA in digital health plays a more efficient role in preventing and controlling COVID-19. It may inspire other countries to adopt this technology in managing the pandemic.

Predicting the Number of People for Meals of an Institutional Foodservice by Applying Machine Learning Methods: S City Hall Case

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu quality deterioration, and preventing rising costs. Compared to other demand forecasts, the menu of dietary personnel includes diverse menus, and various dietary supplements include a range of side dishes. In addition to the menus, diverse subjects for prediction are very difficult problems. Therefore, the purpose of this study was to establish a method for predicting the number of meals including predictive modeling and considering various factors in addition to menus which are actually used in the field. For this purpose, 63 variables in eight categories such as the daily available number of people for the meals, the number of people in the time series, daily menu details, weekdays or seasons, days before or after holidays, weather and temperature, holidays or year-end, and events were identified as decision variables. An ensemble model using six prediction models was then constructed to predict the number of meals. As a result, the prediction error rate was reduced from 10%~11% to approximately 6~7%, which was expected to reduce the residual amount by approximately 40%.

Analysis of Adverse Drug Reaction Reports using Text Mining

BACKGROUND: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. METHODS: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. RESULTS: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. CONCLUSION: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.