Highlighting the Challenges in the Development, Funding, and Reimbursement of Medical Innovations from a Developer Perspective

Healthcare system is an essential system for any nation as it is responsible for maintaining the health of the individuals and public. However, the outbreak of different viral diseases such as influenza, covid-19 etc. has encouraged medical research in different developing and developed countries. Similarly, in Malaysia, different public and private research centers and biotechnology firms are being promoted to develop new and innovative medical drugs and equipment. However, different challenges are faced by the developers in promoting the development and innovations of medical commodities. Thus, this study was conducted to investigate different challenges in the development, funding, and reimbursement of medical innovations in Malaysia. For this purpose, semi-structured interviews were conducted with 7 developers from different public research and development (R&D) centers and biotechnology firms in Malaysia. After the interviews were conducted, their edited transcription was obtained, and thematic analysis was conducted, and different themes and sub-themes were formulated. The results obtained from this study showed that the lack of innovative environment, strategic compliances and effective funding structure negatively influences medical innovations in Malaysia. It has also been observed that poor reimbursement practices and policies and lack of pricing strategies by the Malaysian government impacts the ROI of the associated firms and developers. Thus, it has been recommended that mega-funds and reimbursement policies should be promoted to overcome these challenges in medical innovations.Copyright © 2023 ThinkBiotech LLC. All rights reserved.

Performance comparison of different machine learning algorithms on a time-series of covid-19 data: A case study for Saudi Arabia

In this study we have applied several machine learning algorithms to analyse time-series data related to COVID-19 in Saudi Arabia. We retrieved the data from the official health website of Saudi Arabia for the period March 2nd 2020, to November 27st 2020. Several machine learning models and related algorithms were developed for prediction of total cases and total deaths. The COVID-19 data have been considered as a time-series dataset and the prediction capability of three machine learning methodologies, linear regression, support vector regression and Gaussian process regression, have been compared. When comparing all models based on R2 and RMSE values, it can be inferred that the linear regression and Gaussian process regression models were the most robust models for the prediction of total cases, and total deaths while SVM models were shown less prediction capabilities. Prediction of total cases and total deaths are obtained by taking previous 14 days of time series data as the input to the machine learning algorithms developed in this paper. This study can be helpful in analysing the capabilities of machine learning methodologies for time-series data-sets as well as helping governments in the decision making process for mitigation of the pandemic. © 2021, Scibulcom Ltd.. All rights reserved.