Akas Gajanan Prabhune
@iihmrbangalore.edu.in
Assistant Professor Department of Health IT
Institute of Health Management Research Bangalore
EDUCATION
Bachelor of Dental Surgery, Master of Clinical Resrarch
RESEARCH INTERESTS
Dr. Akash is a Health researcher with 5+ years of experience in Health Systems Strengthening, Digital Health and AI/ML applications in healthcare management. He has worked with various state Governments, Indian Council of Medical Research, several national and international funding agencies
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Akash Prabhune, Vinay R Srihari, Neeraj Kumar Sethiya, and Mansi Gauniyal
Institute of Advanced Engineering and Science
This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.
Sachin S Bhat, Vinay R Srihari, Akash Prabhune, Aishwarya Mallawaram, and Ananya Biswas Bidrohi
IEEE
The paper addresses the critical issue of human resource shortages in healthcare, particularly in developing countries like India. It emphasizes the importance of adequate and well-distributed health professionals for achieving desirable health outcomes. The focus is on quadratic modelling as a tool for optimal planning in healthcare systems, considering the composition and distribution of health personnel.The methodology involves creating a comprehensive unit optimization framework using a Demand Index, Supply Index, and Distance Matrix to derive a Site Suitability Index. The Demand Index integrates demographic, mortality, and footfall data, while the Supply Index considers medical, para-medical, and non-medical staff weights. A Gravity model is employed for accessibility scoring, and a three-by-three matrix is used for rule-based classification. The quadratic optimization model aims to maximize human resource allocation based on physical accessibility, minimizing the cost of deviation from desired healthcare standards. Data mining and cleaning involve secondary data from various sources, subjected to screening using geographical and population data.Results include a Gravity model-derived Baseline Accessibility and Burden Index, categorizing villages and PHCs. The quadratic optimization model allocates resources according to IPHS standards, using a weekly timetable for staff sharing to address shortages. The model successfully optimizes human resource allocation based on demand burden and accessibility, presenting a systematic approach to address healthcare workforce challenges.
Akash Prabhune, Vinay R Srihari, Ananya Biswas Bidrohi, Ashitha Reddy, and Aishwarya Mallawaram
IEEE
This paper introduces a novel approach, a Machine Learning-based Gravity Model, to address this issue by predicting and mitigating disparities in healthcare access based on geographical location, population distribution, transportation infrastructure, and healthcare workforce availability. The research focuses on the Chikkaballapur district in Karnataka, Southern India, as a proof of concept.The methodology involves developing an optimization model for healthcare facility locations by analyzing the geospatial and demographic characteristics of the district. The goal is to maximize accessibility to primary health units while minimizing the burden on these healthcare facilities. To achieve this, the study considers various factors from the demand side (village population, literacy rates, agricultural and non-agricultural workers, OPD and IPD data, among others), supply side (healthcare staff), and distance and travel (including travel friction). Composite indexes are constructed to represent demand, supply, and travel factors, contributing to the overall accessibility and burden calculations.The study classifies villages based on their accessibility scores, separating them into categories of poor, average, and high accessibility. Similarly, Primary Health Centers (PHCs) are classified as underutilized, average, or overutilized based on their burden scores. Using these classifications, the paper presents case scenarios and a rule-based algorithm to recommend actions, such as upgrading or downgrading PHCs and building new ones, aimed at improving accessibility and addressing burden disparities.In conclusion, the Gravity-Based Optimization Model offers a promising solution to the complex challenge of healthcare accessibility and facility optimization in India.
Sachin S Bhat, Vinay R Srihari, Akash Prabhune, Subodh S Satheesh, and Ananya Biswas Bidrohi
IEEE
This article discusses the critical issue of inefficient inventory management and demand forecasting in public healthcare systems, highlighting the significant impact of this problem on patient care and healthcare costs. It introduces a novel approach that leverages machine learning and advanced forecasting techniques to address this challenge. The methodology involves mapping the supply chain, data collection, preprocessing, regression analysis, and the creation of three forecasting models: Projective, Causal, and Stochastic. The results of regression analysis identify key factors affecting stockouts, while the forecasting models predict drug demand and supply requirements. The integration of inventory cost optimization models further enhances the precision of stock requirement forecasts. These models offer a promising solution to improve medication access, reduce stockouts, and optimize resource allocation in primary healthcare services, ultimately leading to more efficient and equitable healthcare delivery in the public sector.
Kalaivani S, Arun Senthilkumar, Akash Prabhune, Mathan Babu Durairaj, and Sachin S Bhat
IEEE
This study focuses on the development of an Electronic Visitor Management System for small-scale hospitals, utilizing an Agile-based Scrum Framework and a Low No-Code (Low Code No Code) platform. The primary aim is to address the challenges of managing crowds and footfalls in hospital in-patient departments (IPD) efficiently. The methodology employed for this project involved the use of the mHealth agile framework, combining clinical product development stages with agile development. A Scrum team was formed, and a comprehensive project timeline was established, with a series of sprints, each with its specific goals and associated product backlog items. The development tools utilized included the Zoho Low Code No Code platform, SQL database, and Android Studio for publishing the application. Results from each sprint are discussed, ranging from defining the project scope to releasing the successful application. The study emphasizes the importance of user consultation for enhancing the User Interface (UI) and User Experience (UX) of the application. Feedback from stakeholders and end-users played a crucial role in refining the product. Overall, this study showcases the successful development of an Electronic Visitor Management System for hospitals, highlighting the role of technology and healthcare professionals in digital health initiatives, reducing development time and costs.
Ritu Ghosh, Aoife Healy, Akash Prabhune, Aishwarya Mallavaram, Sama Raju, and Nachiappan Chockalingam
Informa UK Limited
The COVID-19 pandemic created a challenge for providing assistive technology (AT) and rehabilitation services, with many service providers implementing telehealth service provision for the first time. The objective of this study was to explore the experiences of people accessing and providing AT and rehabilitation services during the pandemic and to assess the implementation of telehealth service delivery at an assistive technology and rehabilitation center in India. A mixed-methods design, combining analysis of clinical data and semi-structured interviews, was utilized. A descriptive analysis of demographics and clinical characteristics of service users accessing services through telehealth, or in-person mode was completed. In addition, service users were interviewed to explore their experiences of accessing services during the pandemic. Service providers were also interviewed to gather their opinions on telehealth service delivery during the pandemic. Findings showed that telehealth was an alternative tool in the pandemic for continuing to deliver services in a low-resource setting. However, not all types of services could be successfully delivered via telehealth. There are barriers to the delivery of telehealth services that need to be considered and addressed to allow successful implementation, and it is important to consider that telehealth consultations are not suitable for all service users.
Akash Prabhune and Aparna Manoharan
Diva Enterprises Private Limited