Data collection as a barrier to personalized medicine

The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.

Does it work? Using a Meta-Impact score to examine global effects in quasi-experimental intervention studies

The evaluation of applied psychological interventions in the workplace or elsewhere is challenging. Randomisation and matching are difficult to achieve and this often results in substantial heterogeneity within intervention and control groups. As a result, traditional comparison of group means using null hypothesis significance testing may mask effects experienced by some participants. Using longitudinal studies of coaching interventions designed to provide support for dyslexic employees, this study describes and evaluates a different approach using a Meta-Impact score. We offer a conceptual rationale for our method, illustrate how this score is calculated and analysed, and show how it highlights person-specific variations in how participants react and respond to interventions. We argue that Meta-Impact is an incremental supplement to traditional variable-centric group-wise comparisons and can more accurately demonstrate in practice the extent to which an intervention worked. Such methods are needed for applied research, where personalized intervention protocols may require impact analysis for policy, legal and ethical purposes, despite modest sample sizes.

Factors Associated With Willingness to Share Health Information: Rapid Review

Background

To expand research and strategies to prevent disease, comprehensive and real-time data are essential. Health data are increasingly available from platforms such as pharmaceuticals, genomics, health care imaging, medical procedures, wearable devices, and internet activity. Further, health data are integrated with an individual’s sociodemographic information, medical conditions, genetics, treatments, and health care. Ultimately, health information generation and flow are controlled by the patient or participant; however, there is a lack of understanding about the factors that influence willingness to share health information. A synthesis of the current literature on the multifactorial nature of health information sharing preferences is required to understand health information exchange.

Objective

The objectives of this review are to identify peer-reviewed literature that reported factors associated with health information sharing and to organize factors into cohesive themes and present a narrative synthesis of factors related to willingness to share health information.

Methods

This review uses a rapid review methodology to gather literature regarding willingness to share health information within the context of eHealth, which includes electronic health records, personal health records, mobile health information, general health information, or information on social determinants of health. MEDLINE and Google Scholar were searched using keywords such as electronic health records AND data sharing OR sharing preference OR willingness to share. The search was limited to any population that excluded health care workers or practitioners, and the participants aged ≥18 years within the US or Canadian context. The data abstraction process using thematic analysis where any factors associated with sharing health information were highlighted and coded inductively within each article. On the basis of shared meaning, the coded factors were collated into major themes.

Results

A total of 26 research articles met our inclusion criteria and were included in the qualitative analysis. The inductive thematic coding process revealed multiple major themes related to sharing health information.

Conclusions

This review emphasized the importance of data generators’ viewpoints and the complex systems of factors that shape their decision to share health information. The themes explored in this study emphasize the importance of trust at multiple levels to develop effective information exchange partnerships. In the case of improving precision health care, addressing the factors presented here that influence willingness to share information can improve sharing capacity for individuals and allow researchers to reorient their methods to address hesitation in sharing health information.

The optimization of combinatorial drug therapies: Strategies and laboratorial platforms

Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.

Improving the Primary Care Consultation for Diabetes and Depression Through Digital Medical Interview Assistant Systems: Narrative Review

Background

Digital medical interview assistant (DMIA) systems, also known as computer-assisted history taking (CAHT) systems, have the potential to improve the quality of care and the medical consultation by exploring more patient-related aspects without time constraints and, therefore, acquiring more and better-quality information prior to the face-to-face consultation. The consultation in primary care is the broadest in terms of the amount of topics to be covered and, at the same time, the shortest in terms of time spent with the patient.

Objective

Our aim is to explore how DMIA systems may be used specifically in the context of primary care, to improve the consultations for diabetes and depression, as exemplars of chronic conditions.

Methods

A narrative review was conducted focusing on (1) the characteristics of the primary care consultation in general, and for diabetes and depression specifically, and (2) the impact of DMIA and CAHT systems on the medical consultation. Through thematic analysis, we identified the characteristics of the primary care consultation that a DMIA system would be able to improve. Based on the identified primary care consultation tasks and the potential benefits of DMIA systems, we developed a sample questionnaire for diabetes and depression to illustrate how such a system may work.

Results

A DMIA system, prior to the first consultation, could aid in the essential primary care tasks of case finding and screening, diagnosing, and, if needed, timely referral to specialists or urgent care. Similarly, for follow-up consultations, this system could aid with the control and monitoring of these conditions, help check for additional health issues, and update the primary care provider about visits to other providers or further testing. Successfully implementing a DMIA system for these tasks would improve the quality of the data obtained, which means earlier diagnosis and treatment. Such a system would improve the use of face-to-face consultation time, thereby streamlining the interaction and allowing the focus to be the patient's needs, which ultimately would lead to better health outcomes and patient satisfaction. However, for such a system to be successfully incorporated, there are important considerations to be taken into account, such as the language to be used and the challenges for implementing eHealth innovations in primary care and health care in general.

Conclusions

Given the benefits explored here, we foresee that DMIA systems could have an important impact in the primary care consultation for diabetes and depression and, potentially, for other chronic conditions. Earlier case finding and a more accurate diagnosis, due to more and better-quality data, paired with improved monitoring of disease progress should improve the quality of care and keep the management of chronic conditions at the primary care level. A somewhat simple, easily scalable technology could go a long way to improve the health of the millions of people affected with chronic conditions, especially if working in conjunction with already-established health technologies such as electronic medical records and clinical decision support systems.

Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85% (for the first dataset) and 92% (for the second dataset) by labeling only 15% of unlabeled data.

Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances.

Electronic Assessment of Physical Decline in Geriatric Cancer Patients

PURPOSE OF REVIEW

The purpose of this review is to explore state-of-the-art remote monitoring and emerging new sensing technologies for in-home physical assessment and their application/potential in cancer care. In addition, we discuss the main functional and non-functional requirements and research challenges of employing such technologies in real-world settings.

RECENT FINDINGS

With rapid growth in aging population, effective and efficient patient care has become an important topic. Advances in remote monitoring and in its forefront in-home physical assessment technologies play a fundamental role in reducing the cost and improving the quality of care by complementing the traditional in-clinic healthcare. However, there is a gap in medical research community regarding the applicability and potential outcomes of such systems. While some studies reported positive outcomes using remote assessment technologies, such as web/smart phone-based self-reports and wearable sensors, the cancer research community is still lacking far behind. Thorough investigation of more advanced technologies in cancer care is warranted.

ABCB1 genetic variants in leukemias: current insights into treatment outcomes

Despite improvements in treatment of different types of leukemia, not all patients respond optimally for a particular treatment. Some treatments will work better for some, while being harmful or ineffective for others. This is due to genetic variation in the form of single-nucleotide polymorphisms (SNPs) that affect gene expression or function and cause inherited interindividual differences in the metabolism and disposition of drugs. Drug transporters are one of the determinants governing the pharmacokinetic profile of chemotherapeutic drugs. The ABCB1 transporter gene transports a wide range of drugs, including drugs used in leukemia treatment. Polymorphisms in the ABCB1 gene do affect intrinsic resistance and pharmacokinetics of several drugs used in leukemia treatment protocols and thereby affect the efficacy of treatment and event-free survival. This review focuses on the impact of three commonly occurring SNPs (1236C>T, 2677G>T/A, and 3435C>T) of ABCB1 on treatment response of various types of leukemia. From the literature available, some of the genotypes and haplotypes of these SNPs have been found to be potential determinants of interindividual variability in drug disposition and pharmacologic response in different types of leukemia. However, due to inconsistencies in the results observed across the studies, additional studies, considering novel genomic methodologies, comprehensive definition of clinical phenotypes, adequate sample size, and uniformity in all the confounding factors, are warranted.

Impact of Membrane Drug Transporters on Resistance to Small-Molecule Tyrosine Kinase Inhibitors

Small-molecule inhibitors of tyrosine kinases (TKIs) are the mainstay of treatment for many malignancies and represent novel treatment options for other diseases such as idiopathic pulmonary fibrosis. Twenty-five TKIs are currently FDA-approved and >130 are being evaluated in clinical trials. Increasing evidence suggests that drug exposure of TKIs may significantly contribute to drug resistance, independently from somatic variation of TKI target genes. Membrane transport proteins may limit the amount of TKI reaching the target cells. This review highlights current knowledge on the basic and clinical pharmacology of membrane transporters involved in TKI disposition and their contribution to drug efficacy and adverse drug effects. In addition to non-genetic and epigenetic factors, genetic variants, particularly rare ones, in transporter genes are promising novel factors to explain interindividual variability in the response to TKI therapy.

An asynchronous multi-view learning approach for activity recognition using wearables

In this paper, we introduce an Asynchronous Multiview Learning (AML) approach to allow accurate transfer of activity classification models across asynchronous sensor views. Our study is motivated by the highly dynamic nature of health monitoring using wearable sensors. Such dynamics include changes in sensing platform (e.g., sensor upgrade) and platform settings (e.g., sampling frequency, on-body sensor location), which result in failure of the machine learning algorithms if they remain untrained in the new setting. Our approach allows machine learning algorithms to automatically reconfigure without any need for labeled training data in the new setting. Our evaluation using real data collected with wearable motion sensors demonstrates that the average classification accuracy using our automatically labeled training data is 85.2%. This accuracy is only 3.4% to 4.5% less than the experimental upper bound, where ground truth labeled training data are used to develop a new activity recognition classifier.

Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes

Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D). In this approach, an Actor-Critic (AC) learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation. AC optimises the daily basal insulin rate and insulin:carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. Automatic, personalised tuning of AC is based on the estimation of information transfer (IT) from insulin to glucose signals. Insulin-to-glucose IT is linked to patient-specific characteristics related to total daily insulin needs and insulin sensitivity (SI). The AC algorithm is evaluated using an FDA-accepted T1D simulator on a large patient database under a complex meal protocol, meal uncertainty and diurnal SI variation. The results showed that 95.66% of time was spent in normoglycaemia in the presence of meal uncertainty and 93.02% when meal uncertainty and SI variation were simultaneously considered. The time spent in hypoglycaemia was 0.27% in both cases. The novel tuning method reduced the risk of severe hypoglycaemia, especially in patients with low SI.

Impact of Genetic Polymorphisms of ABCB1 (MDR1, P-Glycoprotein) on Drug Disposition and Potential Clinical Implications: Update of the Literature

ATP-binding cassette transporter B1 (ABCB1; P-glycoprotein; multidrug resistance protein 1) is an adenosine triphosphate (ATP)-dependent efflux transporter located in the plasma membrane of many different cell types. Numerous structurally unrelated compounds, including drugs and environmental toxins, have been identified as substrates. ABCB1 limits the absorption of xenobiotics from the gut lumen, protects sensitive tissues (e.g. the brain, fetus and testes) from xenobiotics and is involved in biliary and renal secretion of its substrates. In recent years, a large number of polymorphisms of the ABCB1 [ATP-binding cassette, sub-family B (MDR/TAP), member 1] gene have been described. The variants 1236C>T (rs1128503, p.G412G), 2677G>T/A (rs2032582, p.A893S/T) and 3435C>T (rs1045642, p.I1145I) occur at high allele frequencies and create a common haplotype; therefore, they have been most widely studied. This review provides an overview of clinical studies published between 2002 and March 2015. In summary, the effect of ABCB1 variation on P-glycoprotein expression (messenger RNA and protein expression) and/or activity in various tissues (e.g. the liver, gut and heart) appears to be small. Although polymorphisms and haplotypes of ABCB1 have been associated with alterations in drug disposition and drug response, including adverse events with various ABCB1 substrates in different ethnic populations, the results have been majorly conflicting, with limited clinical relevance. Future research activities are warranted, considering a deep-sequencing approach, as well as well-designed clinical studies with appropriate sample sizes to elucidate the impact of rare ABCB1 variants and their potential consequences for effect sizes.

Pharmacogenetics: Implications for Modern Type 2 Diabetes Therapy

Many clinical treatment studies have reported remarkable interindividual variability in the response to pharmaceutical drugs, and uncovered the existence of inadequate treatment response, non-response, and even adverse drug reactions. Pharmacogenetics addresses the impact of genetic variants on treatment outcome including side-effects. In recent years, it has also entered the field of clinical diabetes research. In modern type 2 diabetes therapy, metformin is established as first-line drug. The latest pharmaceutical developments, including incretin mimetics, dipeptidyl peptidase 4 inhibitors (gliptins), and sodium/glucose cotransporter 2 inhibitors (gliflozins), are currently experiencing a marked increase in clinical use, while the prescriptions of α-glucosidase inhibitors, sulfonylureas, meglitinides (glinides), and thiazolidinediones (glitazones) are declining, predominantly because of reported side-effects. This review summarizes the current knowledge about gene-drug interactions observed in therapy studies with the above drugs. We report drug interactions with candidate genes involved in the pharmacokinetics (e.g., drug transporters) and pharmacodynamics (drug targets and downstream signaling steps) of the drugs, with known type 2 diabetes risk genes and previously unknown genes derived from hypothesis-free approaches such as genome-wide association studies. Moreover, some new and promising candidate genes for future pharmacogenetic assessment are highlighted. Finally, we critically appraise the current state of type 2 diabetes pharmacogenetics in the light of its impact on therapeutic decisions, and we refer to major problems, and make suggestions for future efforts in this field to help improve the clinical relevance of the results, and to establish genetically determined treatment failure.

Achieving the World Health Organization's vision for clinical pharmacology

Clinical pharmacology is a medical specialty whose practitioners teach, undertake research, frame policy, give information and advice about the actions and proper uses of medicines in humans and implement that knowledge in clinical practice. It involves a combination of several activities: drug discovery and development, training safe prescribers, providing objective and evidence-based therapeutic information to ethics, regulatory and pricing bodies, supporting patient care in an increasingly subspecialized arena where co-morbidities, polypharmacy, altered pharmacokinetics and drug interactions are common and developing and contributing to medicines policies for Governments. Clinical pharmacologists must advocate drug quality and they must also advocate for sustainability of the Discipline. However for this they need appropriate clinical service and training support. This Commentary discusses strategies to ensure the Discipline is supported by teaching, training and policy organizations, to communicate the full benefits of clinical pharmacology services, put a monetary value on clinical pharmacology services and to grow the clinical pharmacology workforce to support a growing clinical, academic and regulatory need.