Augmented intelligence in precision medicine: Transforming clinical decision‐making with AI/ML and/or quantitative systems pharmacology models

Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance. Show more

Being a 21st-century health care provider is extremely demanding. The growing number of chronic diseases, lack of medical workforce, increasing amounts of administrative tasks, the cost of medical treatment, and rising life expectancy result in an immense challenge for medical professionals. This transformation has been triggered by the growing presence of digital health. Digital health does not only refer to technological transformation; it also fundamentally reshapes the physician-patient relationship and treatment circumstances. We argue that patient empowerment, the spread of digital health, the biopsychosocial-digital approach, and the disappearance of the ivory tower of medicine lead to a new role for physicians. Digital health allows the job of being a medical professional to become more rewarding and creative. The characteristics of a physician-as-idol could shift from self-confident to curious, from rule follower to creative, and from lone hero to team worker. Empowered physicians (e-physicians) can be described as “electronic,” where they use digital technologies in their practice with ease; “enabled,” where they are enabled by regulations and guidelines; and “empowered,” where they are empowered by technologies that support their job and their empowered patients (e-patients). They can be described as “experts” in the use of technologies in their practice or in knowing the best, most reliable, and trustworthy digital health sources and technologies. They can also be described as “engaged,” when understanding the feelings and points of view of their patients, giving relevant feedback, and involving them throughout the whole healing process. The skills and approaches that characterize this era of e-physicians, such as face-to-face communication skills, digital literacy, interdisciplinarity, knowing where to find information, translating large amounts of data into insights for patients, among others, should always have been at the core of practicing medicine. However, the economical, technological, and administrative burden of the profession has not made it possible for most physicians to enjoy the benefits of their training, individual capabilities, and creativity. By understanding how digital health technologies can support or augment their capabilities, physicians would have the chance to practice the art of medicine like never before. Show more

Introduction Physicians are often required to make treatment decisions for patients with Crohn’s disease on the basis of limited objective information about the state of the patient’s gastrointestinal tissue while aiming to achieve mucosal healing. Tools to predict changes in mucosal health with treatment are needed. We evaluated a computational approach integrating a mechanistic model of Crohn’s disease with a responder classifier to predict temporal changes in mucosal health. Methods A hybrid mechanistic–statistical platform was developed to predict biomarker and tissue health time courses in patients with Crohn’s disease. Eligible patients from the VERSIFY study ( n  = 69) were classified into archetypical response cohorts using a decision tree based on early treatment data and baseline characteristics. A virtual patient matching algorithm assigned a digital twin to each patient from their corresponding response cohort. The digital twin was used to forecast response to treatment using the mechanistic model. Results The responder classifier predicted endoscopic remission and mucosal healing for treatment with vedolizumab over 26 weeks, with overall sensitivities of 80% and 75% and overall specificities of 69% and 70%, respectively. Predictions for changes in tissue damage over time in the validation set ( n  = 31), a measure of the overall performance of the platform, were considered good (at least 70% of data points matched), fair (at least 50%), and poor (less than 50%) for 71%, 23%, and 6% of patients, respectively. Conclusion Hybrid computational tools including mechanistic components represent a promising form of decision support that can predict outcomes and patient progress in Crohn’s disease. Supplementary Information The online version contains supplementary material available at 10.1007/s12325-022-02144-y. Show more