Advancing medical affair capabilities and insight generation through machine learning techniques.

BACKGROUND: Pharmaceutical companies are increasingly leveraging machine learning techniques to optimize healthcare research, drug development, and medical affairs activities. AI (artificial intelligence) tools such as chatbots, virtual digital assistants, and research tools have been explored to varying degrees of maturity in industries such as consumer goods or software technology. However, there continues to be untapped opportunities within the pharmaceutical industry to employ these technologies for enhanced engagement and education with healthcare professionals (HCPs). Pharmacists, situated at the crossroads of clinical sciences and innovation, have the potential to elevate their role and significance within the pharmaceutical industry by developing and leveraging such technologies. METHODS: To address this, the python-coded tool, Medical Information (MI) Data Uses For AI Semantic Analysis (MUFASA), utilizes state-of-the-art Sentence Transformer library, clustering, and visualization techniques. MUFASA harnesses unsolicited MI data with AI technology, improving efficiency and providing actionable medical affairs intelligence for targeted content delivery to HCPs. RESULTS: MUFASA optimizes medical affairs activities through its distinctive features: semantic search, cluster analysis, and visualization. Its proficiency in understanding inquiries, as demonstrated through 3D vector mapping and clustering tests, enhances the efficiency of MI and Medical Science Liaison (MSL) case handling. It proves invaluable in training new staff, bolstering response uniformity, and mitigating compliance risks. Leveraging the HDBSCAN algorithm, MUFASA's cluster analysis uncovers deep insights and discerns actionable themes from large inquiry data sets. The visualization graphs, generated from semantic searches, support evidence-based decisions by tracking the effectiveness of initiatives and monitoring trend shifts. Collectively, MUFASA enriches strategic decision-making, cultivates actionable insights, and bolsters healthcare professional engagement. CONCLUSION: There are numerous opportunities for innovation within the intersection of healthcare and data science. Pharmaceutical manufacturers, with one of their medical affairs responsibilities being the collection of unsolicited inquiries, particularly from HCPs, stand poised to leverage machine learning capabilities to optimize its processes. The abundance of data generated by the growing effort to use it in meaningful ways presents an opportunity for pharmaceutical companies to harness machine learning techniques.

3D-Printed Personal Protective Face Shields During the COVID-19 Pandemic: A Survey of Canadian Frontline Workers.

Background During the coronavirus disease 2019 pandemic, three-dimensional (3D) printing was utilized to rapidly produce face shields for frontline workers in response to an acute shortage of personal protective equipment (PPE). In this study, we examine the perceived utility and performance of 3D-printed (3DP) face shields through a survey of frontline workers in Ontario, Canada. Methodology Frontline workers who received community-produced 3DP face shields from the Canadian initiative "3DPPE GTHA" (March-December 2020) were invited to participate in the study. The survey response rate was 54.3%. Of 63 respondents, 39 were patient-facing and 24 were community-facing frontline workers. Participants were asked to rate performance measures in 10 categories on a five-point Likert scale. Data were categorized by organization and frontline worker type, and a t-test was used to determine statistically significant differences among subgroups. Results The mean preference for 3DP face shields among respondents was 3.2 out of 5 (95% confidence interval [CI]: 2.1-4.3). Community-facing respondents reported significantly greater overall utility scores for 3DP face shields (3.58, 95% CI: 3.38-3.79) compared to respondents working in a patient-facing profession (2.95, 95% CI: 2.77-3.13; p < 0.05). However, no differences were reported in portability and compatibility with other PPE. Respondents from organizations with large service volumes reported significantly lower overall utility scores (2.67, 95% CI: 2.44-2.89) than respondents in organizations with smaller service volumes (3.45, 95% CI: 3.28-3.62; p < 0.05). Conclusions Community-facing frontline workers and those from smaller service volume organizations endorse higher utility for 3DP face shields than patient-facing frontline workers. Despite this, frontline workers generally rate 3DP face shields positively. 3DP face shields are a viable option for personal and community use and can be used to supplement supply in a community setting.

Overcoming vaccine deployment challenges among the hardest to reach: lessons from polio elimination in India.

After more than 30 years of efforts to eliminate polio, India was certified polio free by WHO in 2014. The final years prior to polio elimination were characterised by concentrated efforts to vaccinate hard-to-reach groups in the state of Uttar Pradesh, including migrant workers, religious minority Muslims and impoverished communities with poor pre-existing social support systems. This article aims to describe the management strategies employed by India to improve the deployment and acceptance of vaccines among hard-to-reach groups in Uttar Pradesh in the final years prior to polio elimination.Three main management principles contributed to polio elimination among the hardest to reach in Uttar Pradesh: bundling of health services, local stakeholder engagement and accountability mechanisms for public health initiatives. In an effort to market the polio campaign as an authentic health-oriented programme, vaccine acceptance was improved by packaging other basic healthcare services such as routine check-ups and essential medications. India also prioritised local stakeholder engagement by using influential community leaders to reach vaccine hesitant groups. Lastly, the accountability mechanisms developed between non-profit organisations and decision-makers in the field ensured accurate reporting and identified deficiencies in healthcare worker training. The lessons learnt from India's polio vaccination programme have important implications for the implementation of future mass vaccination initiatives, particularly when trying to reach vulnerable communities.

Optimization of community-led 3D printing for the production of protective face shields.

BACKGROUND: As the healthcare system faced an acute shortage of personal protective equipment (PPE) during the COVID-19 pandemic, the use of 3D printing technologies became an innovative method of increasing production capacity to meet this acute need. Due to the emergence of a large number of 3D printed face shield designs and community-led PPE printing initiatives, this case study examines the methods and design best optimized for community printers who may not have the resources or experience to conduct such a thorough analysis. CASE PRESENTATION: We present the optimization of the production of 3D printed face shields by community 3D printers, as part of an initiative aimed at producing PPE for healthcare workers. The face shield frames were manufactured using the 3DVerkstan design and were coupled with an acetate sheet to assemble a complete face shield. Rigorous quality assurance and decontamination protocols ensured community-printed PPE was satisfactory for healthcare use. CONCLUSION: Additive manufacturing is a promising method of producing adequate face shields for frontline health workers because of its versatility and quick up-start time. The optimization of stacking and sanitization protocols allowed 3D printing to feasibly supplement formal public health responses in the face of a global pandemic.