On Educating about Medical Data Management

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

MAV-clic: management, analysis, and visualization of clinical data

Objectives

Develop a multifunctional analytics platform for efficient management and analysis of healthcare data.

Materials and Methods

Management, Analysis, and Visualization of Clinical Data (MAV-clic) is a Health Insurance Portability and Accountability Act of 1996 (HIPAA)-compliant framework based on the Butterfly Model. MAV-clic extracts, cleanses, and encrypts data then restructures and aggregates data in a deidentified format. A graphical user interface allows query, analysis, and visualization of clinical data.

Results

MAV-clic manages healthcare data for over 800 000 subjects at UConn Health. Three analytic capabilities of MAV-clic include: creating cohorts based on specific criteria; performing measurement analysis of subjects with a specific diagnosis and medication; and calculating measure outcomes of subjects over time.

Discussion

MAV-clic supports clinicians and healthcare analysts by efficiently stratifying subjects to understand specific scenarios and optimize decision making.

Conclusion

MAV-clic is founded on the scientific premise that to improve the quality and transition of healthcare, integrative platforms are necessary to analyze heterogeneous clinical, epidemiological, metabolomics, proteomics, and genomics data for precision medicine.

A novel metadata management model to capture consent for record linkage in longitudinal research studies

Background: Informed consent is an important feature of longitudinal research studies as it enables the linking of the baseline participant information with administrative data. The lack of standardized models to capture consent elements can lead to substantial challenges. A structured approach to capturing consent-related metadata can address these. Objectives: a) Explore the state-of-the-art for recording consent; b) Identify key elements of consent required for record linkage; and c) Create and evaluate a novel metadata management model to capture consent-related metadata. Methods: The main methodological components of our work were: a) a systematic literature review and qualitative analysis of consent forms; b) the development and evaluation of a novel metadata model. Discussion: We qualitatively analyzed 61 manuscripts and 30 consent forms. We extracted data elements related to obtaining consent for linkage. We created a novel metadata management model for consent and evaluated it by comparison with the existing standards and by iteratively applying it to case studies. Conclusion: The developed model can facilitate the standardized recording of consent for linkage in longitudinal research studies and enable the linkage of external participant data. Furthermore, it can provide a structured way of recording consent-related metadata and facilitate the harmonization and streamlining of processes.

Systematic review of health data quality management and best practices at community and district levels in LMIC

Research findings have reported lack of reliable health data and poor management for district health information systems in low and middle-income countries (LMIC). This paper aims to review the literature on problems with health data quality management and health information evidences and evidences of best practices and use at community and district levels in LMIC, with a view to making recommendations for future research. Research citations, conference proceedings and diseases surveillance reports from 2000–2011 were accessed in PubMed, Medline, LISTA (EBSCO), CINAHL, Cochrane, and Google. Relevant studies were selected, the methodologies critiqued and synthesized. The researchers accessed 1383, and 38 were reviewed by three reviewers. Poor quality health data, low level of health information use, and poor management of health information systems were found. These findings hinder evidence-based decisions based and planning at community and district levels in LMIC. Though poor practices were found, improved health care services delivery with improved health data efficiency was found to be possible.

Health-enabling technologies for pervasive health care: on services and ICT architecture paradigms

BACKGROUND

Progress in information and communication technologies (ICT) is providing new opportunities for pervasive health care services in aging societies.

OBJECTIVES

To identify starting points of health-enabling technologies for pervasive health care. To describe typical services of and contemporary ICT architecture paradigms for pervasive health care.

METHODS

Summarizing outcomes of literature analyses and results from own research projects in this field.

RESULTS

Basic functions for pervasive health care with respect to home care comprise emergency detection and alarm, disease management, as well as health status feedback and advice. These functions are complemented by optional (non-health care) functions. Four major paradigms for contemporary ICT architectures are person-centered ICT architectures, home-centered ICT architectures, telehealth service-centered ICT architectures and health care institution-centered ICT architectures.

CONCLUSIONS

Health-enabling technologies may lead to both new ways of living and new ways of health care. Both ways are interwoven. This has to be considered for appropriate ICT architectures of sensor-enhanced health information systems. IMIA, the International Medical Informatics Association, may be an appropriate forum for interdisciplinary research exchange on health-enabling technologies for pervasive health care.