This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical savings.

Problem Definition. Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Methodology/Results. In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk. Specifically, we combine counterfactual inference, machine learning, and optimization techniques to build a scalable decision model that can exploit high-dimensional medical data, such as the data found in modern electronic health records. Our decision model is evaluated based on electronic health records from 89,191 prediabetic patients. We compare the allocation of preventive treatments (metformin) prescribed by our data-driven decision model with that of current practice. We find that if our approach is applied to the U.S. population, it can yield annual savings of $1.1 billion. Finally, we analyze the cost-effectiveness under varying budget levels. Managerial Implications. Our work supports decision-making in health management, with the goal of achieving effective disease prevention at lower costs. Importantly, our decision model is generic and can thus be used for effective allocation of preventive care for other preventable diseases.

Artificial intelligence (AI) and big data are transforming healthcare with high-throughput analyses of complex diseases. Machine learning and sophisticated computational methods can be used to efficiently interpret human genomes and other biomarkers, providing insights for patient treatment and with major applications in diagnostics and preventive care. A personalised treatment plan may include preventive care for diseases that are at a higher risk of developing, for example increased screening for cancer if a patient possesses the BRCA 1 or BRCA 2 gene mutation. Additionally, AI can generate insights from genetic information, biomarkers, and other physiological data to predict how a patient will respond to different treatment options, which may help avoid adverse reactions, reduce the use of expensive or unnecessary treatments on patients that are unlikely to respond, and ultimately reduce hospitalisation and outpatient costs. For more information, GlobalData's latest report, Precision and Personalized Medicine – Thematic Research, provides insight into the most prevalent uses of personalised medicine, new applications, and the healthcare, macroeconomic, and technology themes driving growth.

The organizations will use AI and cloud computing to explore opportunities to increase their health IT offerings, according to an Oct. 18 news release. For the initial step in the collaboration, Babylon's digital health offerings will be added to the Microsoft Azure Marketplace. "This collaboration will allow us to build on our existing capabilities so we can proactively support patients through the devices they already own and shift the focus from reactive to proactive healthcare," said Tom McGuinness, corporate vice president of global healthcare and life sciences at Microsoft. "We look forward to bringing our collective expertise and technologies to drive innovation in healthcare and capitalize on the current digital health revolution by improving the personalization, automation and digitization of the healthcare journey for both clinicians and patients." On Oct. 19, Microsoft said it was adding virtual healthcare capabilities that can be used through Epic and Cerner EHRs.

By Ashim Roy For a population of 128 crores, India has 10.12 lakh doctors. Of these, a recent KPMG report says, 74 per cent cater to only a third of the urban residents. In other words, there are only about 2.63 lakh doctors catering to the majority of Indians residing on the fringes of urban settlements and in rural areas. On the other hand, India has more than 90 crore mobile phone connections. Studies show that in 2008 the number of devices connected to the Internet was more than the number of people on Earth!

Among other things, CareSkore wants to use machine learning to anticipate mortality. However, the newly endowed platform is more than just a Facebook poll that tells you how you'll meet your end this Christmas by being squashed by a falling piano. Storm ventures, Cota Capital, Rising Tide Fund and Liquid 2 Ventures are rallying behind the Y Combinator graduate with today's 4.5 million seed round. CareSkore is combining clinical, socio-economic, demographic, and behavioral data to paint a holistic picture of patients that doctors and insurance companies can use to provide better preventative care. The platform is leveraging Google's TensorFlow and Hadoop to cut through massive third party data sets and generate insights by finding relationships between environmental and clinical data.