The Impact of Television Advertisements on Student Decision-Making Process for College Admission: An exploratory study in India

COVID-19 drove universities throughout the world forcing Higher Education Institutions (HEIs) to go online or virtual. Admissions advertising and student recruitment were two of the sectors that were severely hit. Internet-based advertising has to entirely replace the old advertising environment. Although certain institutions were still able to conduct virtual tours, forums, and information sessions, the amount of involvement differed among colleges and universities. An exploratory study was conducted to investigate how effective Internet advertisements were in influencing students' admission decisions. During the academic year, 2020-21 at several institutions in India's western region, data were collected using the snowball sampling approach on 930 freshly enrolled students. The findings show that the efficacy of Internet ads for university admission is negatively correlated with the student's age group. Surprisingly, the study discovered that both rural and urban students were equally interested in online ads, and that family background had no impact on receptiveness to internet advertisements. (*The paper was presented at the 2nd Conference on Business Data Analytics: Innovation in emerging trends in management data analytics. Apeejay School of Management, Dwarka, Delhi, India. November 2021)

An Intelligent Patient Admission Model of Day Surgery Using Heterogeneous Data with Semi-Supervised Learning: A Case Study of Laparoscopic Cholecystectomy (Preprint)

BACKGROUND Day surgery has many advantages including shortening hospital stay, decreasing the risk of hospital-associated infections, and increasing cost efficiency over traditional surgery, it has gained a great reputation and popularity in recent years. However, the patients’ admission criteria of day surgery at present were mainly based on expert experience, which was a lack of scientific evidence. OBJECTIVE Our study is to investigate the day surgery patient’s admission criteria and build an intelligent machine learning model of day surgery patients who underwent laparoscopic cholecystectomy, to ensure patients’ safety and medical quality, providing reference and inspiration for other day surgery admission decisions. METHODS We analyzed the clinical data of day surgery patients who underwent laparoscopic cholecystectomy at West China Hospital from Jan 1st 2009 to Dec 31st 2021 and developed a semi-supervised artificial intelligence algorithm, SDSPA algorithm, which is built by self-training and uses both structured data like patient characteristics and unstructured clinical diagnosis to assist surgeons to make quick admission decisions. RESULTS After comparing several classifiers with self-training in our experiment, the performance of LightGBM with unstructured text processed by BERT were the best, obtaining an accuracy of 0.85 and an f1-score of 0.83, as well as reaching 0.97 on the precision score, which is an important indicator related to patients’ safety. CONCLUSIONS The application of our SDSPA algorithm can make the patient admission of day surgery more intelligent, and maximize the utilization of medical resources while ensuring patients’ safety.

Algorithmic Bias and Access to Opportunities

The chapter discusses the problem of algorithmic bias in decision-making processes that determine access to opportunities, such as recidivism scores, college admission decisions, or loan scores. After describing the technical bases of algorithmic bias, it asks how to evaluate them, drawing on Iris Marion Young’s perspective of structural (in)justice. The focus is in particular on the risk of so-called ‘Matthew effects’, in which privileged individuals gain more advantages, while those who are already disadvantaged suffer further. Some proposed solutions are discussed, with an emphasis on the need to take a broad, interdisciplinary perspective rather than a purely technical perspective. The chapter also replies to the objection that private firms cannot be held responsible for addressing structural injustices and concludes by emphasizing the need for political and social action.

Examining the association between paramedic transport to the emergency department and hospital admission: a population-based cohort study

Background Increasing hospitalization rates present unique challenges to manage limited inpatient bed capacity and services. Transport by paramedics to the emergency department (ED) may influence hospital admission decisions independent of patient need/acuity, though this relationship has not been established. We examined whether mode of transportation to the ED was independently associated with hospital admission. Methods We conducted a retrospective cohort study using the National Ambulatory Care Reporting System (NACRS) from April 1, 2015 to March 31, 2020 in Ontario, Canada. We included all adult patients (≥18 years) who received a triage score in the ED and presented via paramedic transport or self-referral (walk-in). Multivariable binary logistic regression was used to determine the association of mode of transportation between hospital admission, after adjusting for important patient and visit characteristics. Results During the study period, 21,764,640 ED visits were eligible for study inclusion. Approximately one-fifth (18.5%) of all ED visits were transported by paramedics. All-cause hospital admission incidence was greater when transported by paramedics (35.0% vs. 7.5%) and with each decreasing Canadian Triage and Acuity Scale level. Paramedic transport was independently associated with hospital admission (OR = 3.76; 95%CI = 3.74–3.77), in addition to higher medical acuity, older age, male sex, greater than two comorbidities, treatment in an urban setting and discharge diagnoses specific to the circulatory or digestive systems. Conclusions Transport by paramedics to an ED was independently associated with hospital admission as the disposition outcome, when compared against self-referred visits. Our findings highlight patient and visit characteristics associated with hospital admission, and can be used to inform proactive healthcare strategizing for in-patient bed management.

Physicians’ Views and Agreement about Patient- and Context-Related Factors Influencing ICU Admission Decisions: A Prospective Study

Background: Single patient- and context-related factors have been associated with admission decisions to intensive care. How physicians weigh various factors and integrate them into the decision-making process is not well known. Objectives: First, to determine which patient- and context-related factors influence admission decisions according to physicians, and their agreement about these determinants; and second, to examine whether there are differences for patients with and without advanced disease. Method: This study was conducted in one tertiary hospital. Consecutive ICU consultations for medical inpatients were prospectively included. Involved physicians, i.e., internists and intensivists, rated the importance of 13 factors for each decision on a Likert scale (1 = negligible to 5 = predominant). We cross-tabulated these factors by presence or absence of advanced disease and examined the degree of agreement between internists and intensivists using the kappa statistic. Results: Of 201 evaluated patients, 105 (52.2%) had an advanced disease, and 140 (69.7%) were admitted to intensive care. The mean number of important factors per decision was 3.5 (SD 2.4) for intensivists and 4.4 (SD 2.1) for internists. Patient’s comorbidities, quality of life, preferences, and code status were most often mentioned. Inter-rater agreement was low for the whole population and after stratifying for patients with and without advanced disease. Kappa values ranged from 0.02 to 0.34 for all the patients, from −0.05 to 0.42 for patients with advanced disease, and from −0.08 to 0.32 for patients without advanced disease. The best agreement was found for family preferences. Conclusion: Poor agreement between physicians about patient- and context-related determinants of ICU admission suggests a lack of explicitness during the decision-making process. The potential consequences are increased variability and inequity regarding which patients are admitted. Timely advance care planning involving families could help physicians make the decision most concordant with patient preferences.

Age as a merit in admission decisions for higher education

This paper uses register data to study how a particular age reward feature affects admission into two highly competitive study programs: medicine and law. The Norwegian admission system to higher education is centralized, and applicants compete in two quotas: one quota almost entirely based on grade point average from upper secondary education and one quota where students can compete with improved grades and where being older automatically increases the chance of acceptance, by awarding age points. For these study programs, we find that the admission system creates a waiting game, as gaining admission in the second quota is nearly impossible without accumulating a substantial amount of age points. If age predicts completion in higher education, this waiting game might be justified. However, if anything, we find the opposite to be true. Our paper suggests that age should carry less weight in admission decisions and that countries and/or higher education institutions should carefully consider how their admission system affects student incentives and how applicants are selected.