Frontiers of Health Policy: Digital Data and Personalized Medicine

This paper argues that due to two unstoppable mechanisms, some of the most pressing future questions in health policy will relate to the use of digital technologies to analyze data concerning patient health. The first mechanism is the shift away from a system where patient data was essentially temporary and not intended to be reused or easily accessed again, to a new digital world where patient data is easily transferred and accessed repeatedly. The second mechanism is a fundamental deepening of the nature of patient data that enables increased personalization of health care for each individual patient, based on not only their detailed medical history, but also their likely future medical history that can be projected for their genetic makeup. We summarize our research investigating the potential consequences of policies in this new world where patient data is virtually costless to store, share, and individualize. We emphasize that issues of data management and privacy are now at the forefront of health policy considerations. Digital data and digital technologies have the potential to transform medicine through two mechanisms. First, digital patient data is far easier to share and access than traditional paper records. This has many potential upsides, but also raises the question of how the potential benefits of sharing patient data are moderated by privacy concerns. Second, the advent of digital storage has now made it possible to store, virtually costlessly, vast swathes of data about any one individual patient. Such individualized data also enables a patient-centric approach to medicine, often referred to as “personalized” or “precision” medicine, based on that individual patient’s genetic makeup. This article discusses the potential benefits and possible policy consequences of this digital shift. It emphasizes that the benefits of digital technologies are found when data is actually transferred and repeatedly accessed. This emphasizes that policies that wish to encourage the potential upside of digital technologies should emphasize easy data transfer. Empirical evidence suggests that health-care providers may not individually have the right incentives to share data, and therefore if a policy aims to encourage data transfer it needs to not only subsidize the adoption of digital technologies, but also make sure that there are the right incentives to use these technologies to share data. Often, well-meaning policies toward data security and data privacy can hamper this process. This article also suggests that there are distinct concerns related to the deepening and individualizing of data that is associated with personalized medicine, and that while there is potentially a large upside in terms of medical outcomes, the risks associated with this data are unusual. If policymakers seek to encourage personalized medicine, they might be especially successful to employ an approach to data management that gives control of the use of the data to the patient.National Science Foundation (U.S.) (Career Award 6923256

Personalized medicine—a modern approach for the diagnosis and management of hypertension

The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension

Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects

Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them

Machine learning algorithms to predict breast cancer recurrence using structured and unstructured sources from electronic health records

Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone. We collected and preprocessed clinical data from a cohort of BC patients, resulting in 823 valid subjects for analysis. We derived three sets of features: structured information, features from free text, and a combination of both. We evaluated the performance of five ML algorithms to predict 5-year cancer recurrence and selected the best-performing to test our hypothesis. The XGB (eXtreme Gradient Boosting) model yielded the best performance among the five evaluated algorithms, with precision = 0.900, recall = 0.907, F1-score = 0.897, and area under the receiver operating characteristic AUROC = 0.807. The best prediction results were achieved with the structured dataset, followed by the unstructured dataset, while the combined dataset achieved the poorest performance. ML algorithms for BC recurrence prediction are valuable tools to improve patient risk stratification, help with post-cancer monitoring, and plan more effective follow-up. Structured data provides the best results when fed to ML algorithms. However, an approach based on natural language processing offers comparable results while potentially requiring less mapping effort.European Union | Ref. 875406Fondo Europeo de Desarrollo Regional (FEDER)Xunta de Galici

Visit Your Therapist in Metaverse - Designing a Virtual Environment for Mental Health Counselling

The emergence of the metaverse as a virtual world platform has opened up new possibilities for the use of immersive technologies in healthcare. This paper aims to explore the potential of the metaverse for healthcare and show how metaverse should be designed. We conduct a study based on design science research and derive design principles for the designing of a virtual environment for mental health counselling. We evaluate each of these design principles and describe how they can be applied in a practical solution. The results indicate that the metaverse holds significant promise for improving healthcare delivery and enhancing patient outcomes. Our study thus contributes to the emerging field of metaverse in healthcare by providing a design approach for the development of applications that can serve as a virtual environment for therapeutic sessions between medical therapists and patients

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care