How to Become a Smart Patient in the Era of Precision Medicine?

Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework

Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework.

From Individuals to Systems and Contributions to Creations: Novel Framework for Mapping the Efforts of Individuals by Convening The Center of Health and Health Care

Background

People with lived health care experiences (often referred to as “patients”) are increasingly contributing to health care and are most effective when they are involved as partners who can contribute complementary knowledge alongside other stakeholders in health care.

Objective

Convening The Center aimed to bring together “people known as patients”—the center of health care—to address priorities as they defined them.

Methods

According to the original project design, an in-person gathering was to be conducted; however, as a result of the COVID-19 pandemic, the in-person gathering was transformed into a series of digital gatherings, including an in-depth interview phase, small-group gatherings, and a collective convening of 25 participants (22 women and 3 men from the United States, India, Costa Rica, Sweden, and Pakistan). Each participant was interviewed on Zoom (Zoom Video Communications Inc), and the interview data were thematically analyzed to design a subsequent small group and then full cohort Zoom sessions. Visual note-taking was used to reinforce a shared understanding of each individual- and group-level conversation.

Results

The interviews and gatherings for Convening The Center offered unique perspectives on patient activities in research, health innovation, and problem-solving. This project further developed a novel, two-spectrum framework for assessing different experiences that patients may have or seek to gain, based on what patients actually do, and different levels of patients’ involvement, ranging from individual to community to systemic involvement.

Conclusions

The descriptors of patients in academic literature typically focus on what health care providers think patients “are” rather than on what patients “do.” The primary result of this project is a framework for mapping what patients “do” and “where” they do their work along two spectra: from creating their own projects to contributing to work initiated by others and from working at levels ranging from individual to community to systems. A better understanding of these spectra may enable researchers to more effectively engage and leverage patient expertise in health care research and innovation.

Natural Kinase Inhibitors for the Treatment and Management of Endometrial/Uterine Cancer: Preclinical to Clinical Studies

Endometrial cancer (EC) is the sixth most prevalent type of cancer among women. Kinases, enzymes mediating the transfer of adenosine triphosphate (ATP) in several signaling pathways, play a significant role in carcinogenesis and cancer cells’ survival and proliferation. Cyclin-dependent kinases (CDKs) are involved in EC pathogenesis; therefore, CDK inhibitors (CDKin) have a noteworthy therapeutic potential in this type of cancer, particularly in EC type 1. Natural compounds have been used for decades in the treatment of cancer serving as a source of anticancer bioactive molecules. Many phenolic and non-phenolic natural compounds covering flavonoids, stilbenoids, coumarins, biphenyl compounds, alkaloids, glycosides, terpenes, and terpenoids have shown moderate to high effectiveness against CDKin-mediated carcinogenic signaling pathways (PI3K, ERK1/2, Akt, ATM, mTOR, TP53). Pharmaceutical regimens based on two natural compounds, trabectedin and ixabepilone, have been investigated in humans showing short and midterm efficacy as second-line treatments in phase II clinical trials. The purpose of this review is twofold: the authors first provide an overview of the involvement of kinases and kinase inhibitors in the pathogenesis and treatment of EC and then discuss the existing evidence about natural products’ derived kinase inhibitors in the management of the disease and outline relevant future research.

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.

PCaLiStDB: a lifestyle database for precision prevention of prostate cancer

The interaction between genes, lifestyles and environmental factors makes the genesis and progress of prostate cancer (PCa) very heterogeneous. Positive lifestyle is important to the prevention and controlling of PCa. To investigate the relationship between PCa and lifestyle at systems level, we established a PCa related lifestyle database (PCaLiStDB) and collected the PCa-related lifestyles including foods, nutrients, life habits and social and environmental factors as well as associated genes and physiological and biochemical indexes together with the disease phenotypes and drugs. Data format standardization was implemented for the future Lifestyle-Wide Association Studies of PCa (PCa_LWAS). Currently, 2290 single-factor lifestyles and 856 joint effects of two or more lifestyles were collected. Among these, 394 are protective factors, 556 are risk factors, 45 are no-influencing factors, 52 are factors with contradictory views and 1977 factors are lacking effective literatures support. PCaLiStDB is expected to facilitate the prevention and control of PCa, as well as the promotion of mechanistic study of lifestyles on PCa. Database URL: http://www.sysbio.org.cn/pcalistdb/.

Big Data in Medicine, the Present and Hopefully the Future

The emergence of data coming from different venues, as several “omic” approaches, is providing already compelling evidence that the smart use of this information could provide invaluable information to prevent, diagnose and treat human diseases. However, the most daunting challenges remain ahead, as the explosive accumulation of data from additional perspectives, including social graphs, biosensors, and imaging, promise to deliver crucial information that could be exploited for the improvement of the entire human race, both in developed, and developing countries, optimizing health expenses and reaching also the less fortunate sections of the societies. And yet, formidable challenges remain, that pertain for the most part to the collection of the data, their organization, and most relevantly their integration. Here we provide few, pointed examples to the present relevance of these big data approaches in human health as well potential road maps toward the implementation of broader data collections and analyses.

Knowledge about Disease, Medication Therapy, and Related Medication Adherence Levels among Patients with Hypertension

Background and Objectives: A particular problem in cardiology is poor adherence to pharmacological treatment among patients with hypertension. It is known that approximately half of these patients do not use their medications as prescribed by their doctor. Patients may choose not to follow the doctor's recommendations and regularly do not control their blood pressure, owing to many factors. A convenient method for measuring the level of adherence is the Morisky Medication Adherence Scale, which also provides insight into possible remedies for low adherence. We investigated their therapy, knowledge about the disease and its control, and demographic differences to assess the adherence of patients with hypertension. Materials and Methods: This was a cross-sectional observational study. Data were collected through a survey of 12 pharmacies in Latvia. The study involved 187 participants with hypertension. Results: The prevalence of non-adherence was 46.20% in Latvia. The oldest patients were the most adherent (p = 0.001, β = 0.27). The higher the self-rated extent from 0 to 10, to which the patient takes their antihypertensives exactly as instructed by their physician, the higher the level of adherence (p < 0.0001, β = 0.38), where at "0", the patient does not follow physician instructions at all, and at "10", the patient completely follows the physician's instructions. Non-adherent patients tend to assess their medication-taking behavior more critically than adherent patients. The longer the patient is known to suffer from hypertension, the more adherent he or she is (p = 0.014, β = 0.19). Conclusions: Medication non-adherence among patients with hypertension is high in Latvia. Further investigations are needed to better understand the reasons for this and to establish interventions for improving patient outcomes.

Forecasting Single Disease Cost of Cataract Based on Multivariable Regression Analysis and Backpropagation Neural Network

In medical services, charge according to the disease is an important way to promote the reform of pricing mechanism, control the unreasonable growth of medical expenses, as well as reduce the burden on patients. Single disease cost forecasting that both identify potential influencing or driving factors and enable better proactive estimation of costs can guide the management and control of medical costs. This study aimed to identify the factors that affect the medical costs of single disease cataract and compare 2 regression models for anticipating acceptable medical cost forecasts. For this purpose, 483 patients with cataract surgery completed in West China Hospital from May 1, 2015, to October 1, 2015, were selected from hospital information system. For cost forecasting, multivariable regression analysis (MRA) and backpropagation neural network (BPNN) were used. Analysis of data was performed with SPSS21.0 and MATLAB2014a software. Total medical costs of patients with cataract (n = 483) ranged from 2015.00 to 13 359.00 CNY, and the mean ± standard deviation is 6292.29 ± 2639.43 CNY. Factors influencing costs of cataract in the MRA include, in importance order, intraocular lens (IOL) implantation (|r|: 0.805, P < .01), doctor level (|r|: 0.644, P < .01), payment source (|r|: 0.554, P < .01), admission status (|r|: 0.326, P < .01), additional diagnosis (|r|: 0.260, P < .01), type of surgery (|r|: 0.127, P < .05), and type of anesthesia (|r|: 0.126, P < .05). In terms of forecasting performance, BPNN (average error: 2.81%) outperforms, yet is less interpretable than MRA (average error: 5.79%). Both MRA and BPNN are technically and economically feasible in generating medical costs of cataract. And some insights on using results of the forecasting model in controlling and reducing disease costs are obtained.

Characteristics of Future Models of Integrated Outpatient Care

Replacement of fee-for-service with capitation arrangements, forces physicians and institutions to minimize health care costs, while maintaining high-quality care. In this report we described how patients and their families (or caregivers) can work with members of the medical care team to achieve these twin goals of maintaining-and perhaps improving-high-quality care and minimizing costs. We described how increased self-management enables patients and their families/caregivers to provide electronic patient-reported outcomes (i.e., symptoms, events) (ePROs), as frequently as the patient or the medical care team consider appropriate. These capabilities also allow ongoing assessments of physiological measurements/phenomena (mHealth). Remote surveillance of these communications allows longer intervals between (fewer) patient visits to the medical-care team, when this is appropriate, or earlier interventions, when it is appropriate. Systems are now available that alert medical care providers to situations when interventions might be needed.

Caring electronically for young outpatients who have epilepsy

PURPOSE

The purpose of this study was to review electronic tools that might improve the delivery of epilepsy care, reduce medical care costs, and empower families to improve self-management capability.

METHOD

We reviewed the epilepsy-specific literature about self-management, electronic patient-reported or provider-reported outcomes, on-going remote surveillance, and alerting/warning systems.

CONCLUSIONS

The improved care delivery system that we envision includes self-management, electronic patient (or provider)-reported outcomes, on-going remote surveillance, and alerting/warning systems. This system and variants have the potential to reduce seizure burden through improved management, keep children out of the emergency department and hospital, and even reduce the number of outpatient visits.