A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study

PAs in India: A survey report on qualifications, deployment, specialties, and job satisfaction

BACKGROUND

This report is based on a 2023 nationwide survey and literature review of physician associates/assistants (PAs) in India. The National Commission for Allied and Healthcare Professions Act, 2021, which is being implemented in India, included guidelines for PA licensing, creating a unified national curriculum, and preventing malpractice.

METHODS

Using a purposive sampling strategy, we surveyed 536 PAs in India between February 10 and April 30, 2023. The survey collected data on demographics, academic qualifications, specialties, salaries, job satisfaction, and future outlooks through an online questionnaire.

RESULTS

The survey showed that most PAs are below age 30 years, and most graduated PAs were involved in clinical practice in 2023. PAs work in almost all clinical specialties; most practice in private hospitals. States in southern India continued to dominate educating and employing PAs, with a nationwide representation of more than 14 states. More than 80% of practicing PAs are satisfied with their profession; however, the majority are unsatisfied with their wages.

CONCLUSIONS

This first formal survey on India's PAs illustrates the challenges of PA professional practice and standardization of education and licensure in the Indian healthcare system.

M. Cognitive computing technological trends and future research directions in healthcare — A systematic literature review

BACKGROUND AND AIM

Cognitive Computing systems are the intelligent systems that thinks, understands and augments the capabilities of human brain by blending the technologies of Artificial Intelligence, Machine Learning and Natural Language Processing. In recent days, maintenance or enhancement of health by preclusion, prognosis, and analysis of diseases has become a challenging task. The increasing diseases and its causes becomes a big question before humanity. Limited risk analysis, meticulous training process, and automated critical decision-making are some of the issues of cognitive computing. To overcome this issue, cognitive computing in healthcare works like a medical prodigy which anticipates the disease or illness of the human being and helps the doctors with technological facts to take the timely action. The main aim of this survey article is to explore the present and futuristic technological trends of cognitive computing in healthcare. In this work, different cognitive computing applications are reviewed, and the best application is recommended to the clinicians. Based on this recommendation, the clinicians are able to monitor and analyze the physical health of patients.

METHODS

This article presents the systematic literature on the different aspects of cognitive computing in healthcare. Nearly seven online databases such as SCOPUS, IEEE Xplore, Google Scholar, DBLP, Web of Science, Springer and PubMed were screened and the published articles related to cognitive computing in healthcare is collected from 2014 to 2021. In total, 75 articles were selected, examined and their pros and cons are analyzed. The analysis is done with respect to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

The basic findings of this review article and their significance for theory and practice are mindmaps portraying the cognitive computing platforms, cognitive applications in healthcare, and use cases of cognitive computing in healthcare. A detailed discussion section highlighting the present issues, future research directions and recent applications of cognitive computing in healthcare. Accuracy analysis of different cognitive systems conclude that the Medical Sieve achieves 0.95 and Watson For Oncology (WFO) achieves 0.93 and hence proves to be the prominent computing systems for healthcare.

CONCLUSIONS

Cognitive computing, an evolving technology in healthcare augments the clinical thought process and enable the doctors to make the right diagnosis and preserve the patient's health in good condition. These systems provides timely care, optimal and cost-effective treatment. This article provides an extensive survey of the importance of cognitive computing in the health sector by highlighting the platforms, techniques, tools, algorithms, applications, and use cases. This survey also explores about the works in the literature on present issues and proposes the future research directions of applying cognitive systems in healthcare.

Utility-driven assessment of anonymized data via clustering

In this study, clustering is conceived as an auxiliary tool to identify groups of special interest. This approach was applied to a real dataset concerning an entire Portuguese cohort of higher education Law students. Several anonymized clustering scenarios were compared against the original cluster solution. The clustering techniques were explored as data utility models in the context of data anonymization, using k-anonymity and (ε, δ)-differential as privacy models. The purpose was to assess anonymized data utility by standard metrics, by the characteristics of the groups obtained, and the relative risk (a relevant metric in social sciences research). For a matter of self-containment, we present an overview of anonymization and clustering methods. We used a partitional clustering algorithm and analyzed several clustering validity indices to understand to what extent the data structure is preserved, or not, after data anonymization. The results suggest that for low dimensionality/cardinality datasets the anonymization procedure easily jeopardizes the clustering endeavor. In addition, there is evidence that relevant field-of-study estimates obtained from anonymized data are biased.