Value-Based Care and Patient Outcome Measures, with Focus on Chronic Kidney Disease (CKD), Congenital Heart Disease, and Stroke

ICHOM defines several patient-centered outcome measure in relation to value-based care. Family sets of outcome measures in several frontiers of medicine are tailored to specific conditions and address the essential aspects of quality, patient-centeredness, informed decision making, and data-based insights. Three important conditions where outcome measures promote value-based care are discussed i.e. congenital heart disease, stroke, and chronic kidney disease.

Geography: United States; Focus Area: Outcome measures in value-based care

The International Consortium for Health Outcomes Measurement (ICHOM) defines patient-centered outcome measures, drives adoption, and reports measures worldwide to enhance value-based care (Porter, 2019). Such a definition of outcomes enables patients to inquire regarding “meaningful outcomes” and enables “data-driven answers“ for doctors. Outcome measures promote several essential aspects of value-based care including quality improvement (performance evaluation of physicians on a global basis, learning opportunities, and care improvement), informed decision-making (patient autonomy in choosing physicians and appropriate treatment), and reduced costs (high quality care and service-based costs).

Patient-centered outcome measures defined by ICHOM are rolled out as family of sets, for several categories including congenital anomalies, cardiometabolic, neurology, oncology, renal and urogenital, maternal and child health, gastrointestinal, infectious disease, mental health, life course, neurology, musculoskeletal, and ophthalmological. Standardized patient-centered outcome measurement sets consist of state-of-the-art  reviews specific to the condition, covering context-specific areas including burden of care, survival, patient-centered well-being, and treatment specific outcomes to name a few (Porter, 2019). The following article discusses the organization of standardized outcome measures for chronic kidney disease (CKD), congenital heart disease, and stroke validated by an interdisciplinary and geographically distributed team.

Outcome Measures of Chronic Kidney Disease

Chronic kidney disease (CKD) has a high prevalence (8%-16% in the general population) and leads to high costs of healthcare, poor health-related quality of life (QoL), and adverse health outcomes (Verberne et al., 2019). It contributes towards four main communicable diseases i.e. cancer, chronic respiratory disease,  diabetes, and cardiovascular disease. 

The working group for CKD outcome measures consisted of 22 members from 9 countries (including CKD registry experts, clinicians such as transplant surgeons and nephrologists, kidney care providers, epidemiologists, and research scientists). 76 outcome measures were identified after surveying literature systematically, gaining input from patient advisory groups, and assessing registries. Thereafter, 19 outcome domains were identified - 9 for CKD patients and 10 for treatment-specific groups. The outcome measures are set up for multiple treatment approaches including transplant, conservative care, pre-RRT, peritoneal dialysis, and hemodialysis. The outcome measures covered for CKD are related to patient-reported health and wellbeing, survival, burden of care, and treatment-specific outcomes (ICHOM, 2023a; Verberne et al., 2019). 

• The evaluations for pain, fatigue, health-related QoL (HRQoL), and physical function come under patient-reported health and well-being. 
• As part of survival, clinicians measure vital status. 
• Cardiovascular events and hospitalizations are measures for burden of care. 
• Finally, treatment specific outcomes include kidney allograft survival, malignancy, vascular access survival, renal function or eGFR, and peritoneal dialysis modality survival. 

CKD requires outcome measures as there is no standardized approach to report CKD outcomes of care. Biochemical markers are collected for patients, however, HRQoL measures are recorded rarely. The measures were defined with the objective of improving care quality through the use of “identical, meaningful, and patient-relevant” care outcomes in routine clinical practice (Verberne et al., 2019).

Promotion: Quality of Life: The Assessment, Analysis and Reporting of Patient-reported Outcomes 3rd Edition, Kindle Edition by Peter M. Fayers (Author), David Machin (Author) 

     

Outcome Measures of Stroke

The ICHOM patient-centered outcome measures for stroke include the components of mood and cognitive function, social participation and communication, fatigue and pain, self care and grooming, feeding, and mobility (ICHOM, 2021). Outcome measures for stroke are designed for four treatment approaches - Thrombectomy, IV Thrombolysis, Procoagulant Reversal Therapy, and Hemicraniectomy. The treatment variable measured is Symptomatic Intracranial Hemorrhage. In terms of survival and disease and control, guidelines for clinicians are defined for report of new stroke within 90 days after stroke discharge, vital status, and smoking cessation. Patient reported health status is measured in terms of motor functioning, non-motor functioning, cognitive and psychiatric functioning, general health status, social functioning, and health-related quality of life (HRQoL) (ICHOM, 2023b). 

The international standard set of patient-centered outcome measures for stroke (broadly, disease control, survival, long-term life quality, and treatment complications) are designed to assess value in stroke management and quality rather than cost. Stroke outcome measures address the growing health concerns globally including disparities with respect to low-income countries and reduce burden on the society (Salinas et al., 2016). The team of clinical experts specializing in stroke registers, outcomes, epidemiology, global health, and rehabilitation, after arriving at an inclusion and exclusion criteria, defined several outcomes in specific domains to be implemented in different healthcare settings to promote equitable, effective, value-based, and patient-centered care globally.

Researchers further studied psychometric properties of patient reported outcome measures (PROMs) as one of the domains of health-related quality of life (HRQoL) (Philipp et al., 2021). PROMs measure the health status of patients through an assessment of health domains such as mental well-being, psychosocial functioning, functional impairment, and quality of life. The assessment of PROMs has a positive impact on health outcomes, patient satisfaction, and process of care towards a paradigm of patient-centered care. 

The Patient Reported Outcomes Measurement Information System (PROMIS) is a standardized system for the evaluation of PROMs, designed by the National Institute of Health (NIH). The ICHOM Standard Set for Stroke (SSS) incorporates PROMIS-10 measures for an assessment of patient-reported health status based on a two-factor structure measuring global physical health score (GPH) and global mental health score (GMS) (Philipp et al., 2021). These measures may need further scrutiny and in-depth investigation to understand the consensus between clinician assessment and self-report. 

Outcome Measures of Congenital Heart Disease

Patient-centered outcome measures for congenital disease finalized by ICHOM are targeted at improving adult quality of life, pediatric quality of life, adult HRQoL or perceived health status, pediatric HRQoL, depression, anxiety, and productivity. The ICHOM set of patient-centered outcome measures for congenital heart disease include overall health (QoL, HRQoL / perceived health status), social health (financial burden, productivity), mental health (development, anxiety, depression), and physical health (arrhythmias, pregnancy, heart failure, vital status, activity level, growth, and development) (ICHOM Connect, 2023). The congenital heart disease outcome measurement set covers treatment approaches for mental health, physical health, social health, and overall health (ICHOM, 2023c).

The ICHOM congenital heart disease working group developed the “stakeholder-informed standard set of outcomes” for congenital heart disease to serve as benchmarks for health systems across the globe. A scoping review identified outcome domains from 42 registries. Guidelines were selected from three expert-panels - American Heart Association / American College of Cardiology Adult CHD Guidelines, American College of Cardiology / American Academy of Pediatrics Policy Statement for Care of Children with CHD, and European Society of Cardiology Guideline for Management of Group up CHD (Hummel et al., 2021). A literature review of more than 500 articles yielded outcome classifications in mental, physical, social, and overall health function,  which were stratified by current and future health state, clinical and patient reported outcomes, and effect modifiers, for adult and pediatric ages wherever possible. CHD being a chronic lifelong condition leads to acute healthcare utilization. The outcome measurement set serves as a guide to assist in clinical decision-making, making comparisons between health systems, and leveraging quality improvement, towards a culture of value-based care.

Therefore, ICHOM defines specific outcome measures for different conditions as a means to cut costs, improve quality of care, and promote patient-centered care towards a value-based care culture. Extensive research-driven family sets of outcome measures empowers clinicians with toolkits that optimize healthcare utilization and make informed decisions. 

Promotion: Measuring Health-Related Quality of Life in Children and Adolescents: Implications for Research and Practice Hardcover – 1 May 1998 by Dennis Drotar (Editor)

     

Keywords

shared decision making, healthcare quality, chronic kidney disease, patient-centered outcomes, Value-based care, congenital heart disease, patient reported outcome measures (PROMs), CKD, stroke

References

Hummel, K., Whittaker, S., Sillett, N., Basken, A., Berghammer, M., Chalela, T., Chauhan, J., Garcia, L. A., Hasan, B., Jenkins, K., Ladak, L. A., Madsen, N., March, A., Pearson, D., Schwartz, S. M., St Louis, J. D., van Beynum, I., Verstappen, A., Williams, R., & Zheleva, B. (2021). Development of an international standard set of clinical and patient-reported outcomes for children and adults with congenital heart disease: a report from the International Consortium for Health Outcomes Measurement Congenital Heart Disease Working Group. European Heart Journal - Quality of Care and Clinical Outcomes, 7(4), 354–365. https://doi.org/10.1093/ehjqcco/qcab009

ICHOM. (2023a). Chronic Kidney Disease. In ICHOM Connect. ICHOM. https://connect.ichom.org/wp-content/uploads/2023/03/06-Chronic-Kidney-Disease-Flyer.pdf

ICHOM. (2023b). Stroke. In ICHOM Connect. ICHOM. https://connect.ichom.org/wp-content/uploads/2023/03/28-Stroke-Flyer.pdf

ICHOM. (2023). Congenital Heart Disease. https://connect.ichom.org/wp-content/uploads/2023/03/31-Congenital-Heart-Disease-Flyer.pdf

ICHOM Connect. (2023). Congenital Heart Disease. Connect.ichom.org. https://connect.ichom.org/patient-centered-outcome-measures/congenital-heart-disease/

ICHOM. (2021). Stroke – ICHOM Connect. Connect.ichom.org. https://connect.ichom.org/patient-centered-outcome-measures/stroke/

Philipp, R., Lebherz, L., Thomalla, G., Härter, M., Appelbohm, H., Frese, M., & Kriston, L. (2021). Psychometric properties of a patient‐reported outcome set in acute stroke patients. Brain and Behavior, 11(8). https://doi.org/10.1002/brb3.2249

Porter, M. (2019). Value-Based Health Care - Institute For Strategy And Competitiveness. Hbs.edu; Harvard Business School. https://www.isc.hbs.edu/health-care/value-based-health-care/Pages/default.aspx

Salinas, J., Sprinkhuizen, S. M., Ackerson, T., Bernhardt, J., Davie, C., George, M. G., Gething, S., Kelly, A. G., Lindsay, P., Liu, L., Martins, S. C. O., Morgan, L., Norrving, B., Ribbers, G. M., Silver, F. L., Smith, E. E., Williams, L. S., & Schwamm, L. H. (2016). An International Standard Set of Patient-Centered Outcome Measures After Stroke. Stroke, 47(1), 180–186. https://doi.org/10.1161/strokeaha.115.010898

Verberne, W. R., Das-Gupta, Z., Allegretti, A. S., Bart, H. A. J., van Biesen, W., García-García, G., Gibbons, E., Parra, E., Hemmelder, M. H., Jager, K. J., Ketteler, M., Roberts, C., Al Rohani, M., Salt, M. J., Stopper, A., Terkivatan, T., Tuttle, K. R., Yang, C.-W., Wheeler, D. C., & Bos, W. J. W. (2019). Development of an International Standard Set of Value-Based Outcome Measures for Patients With Chronic Kidney Disease: A Report of the International Consortium for Health Outcomes Measurement (ICHOM) CKD Working Group. American Journal of Kidney Diseases, 73(3), 372–384. https://doi.org/10.1053/j.ajkd.2018.10.007

Trauma-Informed Curriculums and Trauma-Sensitive Schools

Trauma-informed curriculums promote resilience in classrooms. Childhood trauma may present in different ways, including inherited and vicarious trauma. It may create unmet needs and the requirement for individualized education. The tools to ensure successful learning are backed by trauma-informed teaching practices, leading to the design of trauma-sensitive schools.

Geography: Global; Focus Area: Trauma-informed curriculums and trauma-sensitive schools

Trauma-informed curriculums initiate healing mechanisms in classrooms to restore behavioral responses, nurture learners, and promote resilience. Trauma exposure in students may adversely influence academic performance and lead to aggressive behavior. It may lead to power imbalance and hypervigilance within classrooms, suspension and engagement with the criminal justice system, and may also lower life quality and life expectancy. Chronic stress manifests in several forms including oppositional behavior, separation anxiety, post traumatic stress disorder (PTSD), depression, and suicide ideation. (Weinsteiger Guzman, 2023) explains how trauma-informed care leads to trauma-informed curriculum and trauma-sensitive schools. 

Key Elements for Trauma-Sensitive Schools: Inherited and Vicarious Trauma, Trauma-Informed Teaching, Trauma-Informed Curriculum, Trauma-Informed Interventions, and Individualized Education Programs

Trauma during childhood is endemic, with the CDC reporting one in seven children experiencing neglect or abuse during the previous year (2022 data).

• Adverse childhood events lead to toxic stress and harm the developing brain of the child, and therefore, the overall health. 
• Traumas manifest in many forms such as detachment, rage, people-pleasing attitude, or perfectionism. 
• Trauma exposure lowers performance in reading, math, and science and raises the chances of an individualized education program (IEP) three-fold.
• Children may not necessarily experience directly, but may have witnessed trauma or have biological parents exposed to trauma, which may manifest as vicarious or intergenerational trauma in learners.
• The endemic nature of trauma during childhood requires interventions. Trauma-informed interventions are strength-based approaches with compassionate interaction. 
• Trauma-informed care practices promote a culture of empowerment, safety, and healing. The same ideology is applied to trauma-informed teaching. 
• A trauma-informed teaching environment promotes positive relationships, warmth, respect, and empathy for learners regardless of the response of the learners. 
• Inherited trauma is far more complex than the diagnostic criteria covered in the latest Diagnostic and Statistical Manual (DSM)
• Trauma-informed teaching practices are a paradigm shift, rooted in resilience.
• Trauma-informed curriculums are a response to the unmet needs of students and define new pathways in classrooms. 

Promotion:  Developing a Trauma-Informed Perspective in School Communities: An Introduction for Educators, School Counselors, and Administrators Hardcover –  by Lynn Heramis (Author)

Promotion: Identifying, Treating, and Preventing Childhood Trauma in Rural Communities (Advances in Psychology, Mental Health, and Behavioral Studies) Hardcover – by Marion Baker (Editor), Jacqueline Ford (Editor), Brittany Canfield (Editor), Traci Grabb (Editor)

Guzman’s Creative Writing Curriculum: An Example of Trauma-Sensitive Curriculum

Weinsteiger Guzman (2023) recommends a trauma-sensitive curriculum grounded in creative writing to promote social-emotional development and academic attitudes. The following key aspects were implemented:

• Narrative reflection was a means to address childhood exposure to trauma.
• Creative writing sessions supported the application of trauma-informed concepts including empathy, emotional intelligence, agency, empowerment, and resilience. 
• The design and delivery of curriculum as well as data analysis was guided by transformational learning theory (the three dimensions of perspective transformation i.e. behavioral, psychological, and conviction) and narrative theory (an analytical technique to articulate life experiences in the structure of a story). 
• Conclusions were based on narratives of students from sixth to twelfth grade from settings that predisposed them to adverse childhood experiences such as community schools and detention facilities. Experts believed that narratives activated memory so that it was possible to imagine a different outcome and achieve the desired change.
• The proposed trauma-informed curriculum was suitable for a variety of settings, at the local level, within classrooms using the inclusive model, and after school.
• The curriculum was revised using iterations before and after a twelve-week period. Survey data helped understand social-emotional development and academic behaviors. Adjustments to the curriculum are enabled through personalized decisions based on learner interests. The overall curriculum cycle was mediated through action research. 
• Restorative justice is an important aspect of trauma-informed curriculum as it is contrary to the “zero tolerance” principle for behavioral policies and practices. It consists of relationship-centered approaches to address and avoid harm, a means to respond to human rights and legal violations, and collaborative problem-solving.

Trauma-informed curriculums are suitable for all students, and serve as an alternative means of promoting equity, safety and empowerment. It is important to implement trauma-informed curricula across educational settings as a means of access to healing for learners, as suspended learners will not have access to healthy relationship skills, social-emotional skills, and mentoring and after-school programs. 

Promotion: Trauma Informed Behaviour Support: A Practical Guide to Developing Resilient Learners Paperback – Import, 25 August 2021 by Dr Edd Kay Ayre (Author), Dr Govind Krishnamoorthy PhD (Author), Dave Ziegler PhD (Foreword)

Promotion: Trauma-Informed Practices for Early Childhood Educators: Relationship-Based Approaches that Support Healing and Build Resilience in Young Children 1st Edition, Kindle Edition by Julie Nicholson (Author), Linda Perez (Author), Julie Kurtz (Author)  Format: Kindle Edition

     

Keywords

empowerment, vicarious trauma, individualized education program, trauma-informed, safety, inherited trauma, trauma-informed teaching, trauma-informed curriculum, learning, trauma-sensitive school, resilience

References

Weinsteiger Guzman, N. (2023). Writing as Transformation: An Action Research Study on Trauma-Informed Curriculum. University of the Pacific Theses and Dissertations, 167. https://scholarlycommons.pacific.edu/uop_etds/3839/

Promotion:

Trauma Informed Schools

Trauma Informed Curriculums

Trauma Informed Communities

Lessons from NHS Patient Safety Guidelines

A culture of patient safety must be the primary healthcare goal of any organization targeting continuous quality improvement. NHS defines the elements of improvement, insight, and involvement as the three important goals to achieve a patient safety culture. Patient safety frameworks promote psychological safety, team work, and set safety standards. Safety improvement is possible in primary and community settings through integrated care pathways in a patient-centered manner. The Improvement Academy of the NHS implements a patient safety culture through multiple networks including the Acute Sepsis Network, Patient Safety Collaborate Network, and Quality Improvement Trainers Network to reduce harm, improve quality and safety, and execute initiatives across multiple domains. 

Geography: United Kingdom; Focus Area: Patient safety

Patient safety is the primary healthcare goal at the NHS. According to the NHS, patient safety is not an individual effort, and promoting continuous improvement in patient safety requires a patient safety system and patient safety culture. In this connection, three strategic goals support the purpose: involvement, insight, and improvement (NHS England & NHS Improvement, 2019). 

• Insight refers to deriving intelligence from multiple sources of patient safety information to improve the understanding of safety - the use of safety measurement, culture metrics, safety learning systems, medical examiner system, improve response to emerging risks, share litigation insight and prevent harm, and implement the Patient Safety Incident Response Framework

• Improvement refers to the design and execution of programs for effective and continued change in the fields that require most attention - deliver the National Patient Safety and Improvement program, Maternal and Neonatal Safety Improvement program, Mental Health Safety Improvement program, Medicines Safety Improvement program, support safety programs for older people, people with learning disabilities, improve safety in the area of microbial resistance, and promote research and innovation to support safety improvement

• Involvement refers to assisting partners, patients, and staff with the opportunities and skills that will improve patient safety for the whole system - involve patient, carers, and families to promote safer care, create system-wide patient safety training and education framework, train people to respond to positive and negative events, assist safety specialists to lead safety improvement, and ensure healthcare system involvement in safety agenda

Promotion: Washington Manual of Patient Safety and Quality Improvement Paperback – 1 January 2016 by Fondahn (Author)

  

In a patient safety framework, psychological safety of staff is promoted, diversity is respected, vision and leadership is promoted at all levels, and openness and teamwork is supported for learning. Regulations are required to check whether safety systems meet the required standards (NHS England & NHS Improvement, 2019). Regulatory organizations such as the Nursing and Midwifery Council, General Medical Council, Health and Care Professions Council, Care Quality Commission (CQC), and  Medicines and Healthcare products Regulatory Agency (MHRA) set standards in their respective areas.

Safety improvement has different meanings in diverse healthcare contexts. In primary and community care, integrated care pathways are utilized to promote accountability and safety. Likewise, digital strategies, medication safety will enable safe care (Improvement Academy, 2021). The Improvement Academy of the NHS implements a practical, flexible, and collaborative approach in partnership with stakeholders to improve patient and staff experience through patient-centered care, apply behavioral change methods to address quality improvement challenges, utilize evidence based interventions to reduce patient harm and improve communication and safety culture, provide practical implementation expertise to health services and researchers, measure and benchmark team safety culture, and understand human psychology to keep patients safe.

Promotion: Patient Safety Ethics: How Vigilance, Mindfulness, Compliance, and Humility Can Make Healthcare Safer 1st Edition, Kindle Edition by John D. Banja (Author)

     

The Improvement Academy operates through multiple networks to address major healthcare concerns (Improvement Academy, 2021):

• The Patient Safety Collaborative Network enables improvement in health and social care. It is a platform to discuss challenges, share ideas, and collaborate to expand networking opportunities that will support its initiatives. The patient safety network operates through multiple themes including mental health, medication safety, deterioration management (through patient safety networks, emergency department networks, and sepsis network), maternal and neonatal health, and adopt and spread network (through COPD and asthma acute hospital discharge network, emergency laparotomy collaborative, and tracheostomy care).

• The Acute Sepsis Network is engaged in supporting safe care for people living with sepsis. Injury to tissues and organs due to sepsis is a growing concern. The Improvement Academy manages this through the involvement of multiple programmes including Managing Deterioration Safety Improvement Programme (improve safety in care homes, test interventions for early recognition of deterioration, improve communication and response in care homes with a focus on recognition, response, and communication, support advance care plans, primary care networks, and patient safety networks), System Safety Improvement Programme (mobilize effective Patient Safety Improvement Networks and support the Patient Safety Incident Response Framework), and Patient Safety Collaborative (reduce harm and variation, share learning).

• The Quality Improvement Trainers Network provides training and professional development as a platform for continuous learning. Quality Improvement training equips leaders with the skills and knowledge required for operational and strategic support for a sustainable improvement culture within the organization. Quality improvement is the combined training consisting of human factors, behavior change, utilizing data in improving healthcare, in addition to improving quality.

In conclusion, the Improvement Academy of the NHS provides resources to nurses, midwives, doctors, allied health professionals, social care workers, managers, and home care staff to promote patient safety and reduce harm through a multitude of initiatives spread across domains, from specialty care, to emerging infectious diseases, to patient experience, safety, and quality improvement.

Promotion: Handbook Of Healthcare Quality & Patient Safety Paperback – 1 January 2017 by Gyani J Girdhar (Author)

Promotion: Zero Harm: How to Achieve Patient and Workforce Safety in Healthcare Audible Logo Audible Audiobook – Unabridged by Craig Clapper (Author), James Merlino (Author), Carole Stockmeier (Author), Gary MacFadden (Narrator), McGraw Hill-Ascent Audio (Publisher)

     

Keywords

NHS Improvement, learning systems, patient safety, prevent harm, quality improvement, safety, safety measurement, safety improvement, NHS England, accountability

References

Improvement Academy. (2021). Improvement Academy. Improvementacademy.org. http://www.improvementacademy.org

NHS England, & NHS Improvement. (2019). The NHS Patient Safety Strategy. In Improvement Academy. https://www.improvementacademy.org/wp-content/uploads/2022/04/Report-template-NHSI-website-for-PSC-Page.pdf

The Applicability of Big Data in Personalized Medicine

Sequencing data, imaging data, clinical data, and data from cancer registries presents several challenges in terms of heterogeneity and a lack of precision. Big analytics serve several purposes including identifying complex data representations, optimization of algorithms, traceability, and predictability. Big data analytics in healthcare context solve several problems such as interpreting findings in personalized medicine and identifying care patterns.

Geography: Global; Focus Area: Healthcare data analysis for precision medicine

Biomedical research that is driven by omics approaches is the future of personalized medicine. Cirillo and Valencia (2019) discuss how cloud infrastructures will serve as foundations of data analysis for personalized medicine through the application of deep learning and machine learning techniques. 

The availability of large-scale clinical and molecular data is a significant challenge for data analysis and interpretation (Cirillo & Valencia, 2019). The success of personalized medicine will depend on the efficiency of data-driven systems to generate mechanistic models for the design of clinical procedures.

International Data Sources for Personalized Medicine

The availability of data has been driven by several factors (Cirillo & Valencia, 2019):

• High throughput genome sequencing at low costs and increased access

• Availability of information extracted from imaging data and clinical records

• Massive efforts in community-based data collection through global initiatives such as Global Alliance for Genomics and Health (GA4GH), big data to knowledge (BD2K) initiative, and ELIXIR research infrastructure

• Domain-specific initiatives such as International Rare Disease Research Consortium (IRDiRC) and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)

• Genomic and epigenetics data from Human Genome Diversity Project (HGDP), Encyclopedia of DNA Elements (ENCODE), Global Network of Personal Genome Projects (PGP), International Human Epigenome Consortium (IHEC), and NIH Roadmap Epigenomics Mapping Consortium (Roadmap)

• Cancer registries including International Cancer Genome Consortium (ICGC) and the Cancer Genome Atlas (TCGA)

Promotion: Genomics and Personalized Medicine: What Everyone Needs to Know® Paperback – Illustrated, 24 March 2016 by Michael Snyder (Author)

   

Challenges Related to Big Data Analytics

Big data is multi-spectral, heterogeneous, incomplete, and imprecise. Analytics for big data require capabilities to interpret complex data representations, algorithm optimization, modeling, and increased computational power (Cirillo & Valencia, 2019). Analytics systems for unstructured data need to support prediction, traceability, and decision support, and be able to identify patterns of care. Functional characterization of medical data often proceeds at a slower pace when compared to data gathering, and increased challenges are encountered when multi-omics data has to be combined with phenotypic patient data to arrive at interpretations in personalized medicine. 

Multiple Data Types Available from Big Data

High volume, sensitive, and complex patient data, such as neuroimaging and genomics data is growing by petabytes annually. This data is accompanied by metadata and quantitative measurements. Heterogeneous data requires scalable and integrative platforms. Common data types available to explore frontiers in personalized include (Cirillo & Valencia, 2019):

• Symptom descriptions (unstructured data)

• ICD codes (structured data)

• Whole genome sequencing data (WGS data)

• Whole exome sequencing data (WEX)

• Transcriptome sequencing data

• Proteome and interactome profiling data

• EHR patient data

• Imaging data

• Multi-omics data

• Data from implants and wearable devices

• Patient generated health data (PGHD)

• Real-time data from sensors and biometric devices

• Mobile-tracked data such as treatment history and lifestyle choices

Promotion: Visualizing Health and Healthcare Data: Creating Clear and Compelling Visualizations to "See how You're Doing" Paperback – Import, 17 December 2020 by K Rowell (Author)

   

Heterogeneity in Big Data Applications

Big data has been applied to multiple disciplines in medicine including cancer and rare disease research, biomarker and drug development, diabetes and cardiovascular disease research, and neurodegeneration studies. Deciphering distributed data collaborative effort (Cirillo & Valencia, 2019). Some examples of personalized medicine projects are personalized brain models of the Human Brain project undertaken by the European Commission. Likewise, the Human Genome project forms the basis of large-scale biomedical projects, and the International Personalized Medicine consortium (PerMed) facilitates the study of mechanisms of hematopoiesis and epigenetics. Data handling is regulated by ethical framework for anonymous processing, such as General Data Protection Regulation (EU). The effectiveness of biomedical data rests on certain characteristics beyond security, such as accessibility, ease of finding data, interoperability, and reusability (FAIR).

Complex Data Analysis

Data analysis requires high performance computing (HPC) for the extraction of knowledge from big data. Computing capabilities such as streaming data analysis, data-intensive simulations, machine learning, deep learning, and neural networks for high dimensional data, and the investigation of multi-view data through data-driven integrated workflows promote complex data analysis (Cirillo & Valencia, 2019). 

Deep learning methods (convolutional / neural networks and recursive neural networks) have interesting applications in prediction and classification such as assessing disease risk, hospital outcome prediction, medical image analysis, prediction of promoter-enhancer interaction and transcription factor binding site, metagenome classification, drug design, and epileptic seizure prediction (Cirillo & Valencia, 2019). 

Therefore, big data can be transformational in healthcare and biomedical research. Big data types and analytics are highly challenging and require advanced machine learning methods to arrive at effective inferences. Continued research and development in these areas will lead to innovative solutions in the field of personalized medicine. 

Promotion: Big Data in Healthcare: Statistical Analysis of the Electronic Health Record Hardcover – 28 February 2020 by Farrokh Alemi (Author)

   

Keywords

healthcare data, data analysis, big data analytics, heterogeneity, complex data patterns, precision medicine, big data, big data frameworks, personalized medicine

References

Cirillo, D., & Valencia, A. (2019). Big data analytics for personalized medicine. Current Opinion in Biotechnology, 58, 161–167. https://doi.org/10.1016/j.copbio.2019.03.004

Big Data Analytics for Building Healthcare Frameworks

Structured and unstructured healthcare data is important for monitoring disease and the health of populations, and facilitating decision-making in precision medicine. Researchers define specific characteristics of healthcare big data to effectively utilize it for multiple purposes. In this connection, big data frameworks allow the extraction of continuous data, data analysis, and personalizing data. As a result, high-performance systems can convert large data volumes into actionable insights for improving the health of populations and improving care quality at reduced costs.

Geography: Global; Focus Area: Healthcare big data for improving care and reducing costs

Structured and Unstructured Healthcare Big Data

Healthcare big data is recorded and stored as structured data (disease terminology, symptom and diagnostic information, patient admission history, laboratory results, services availed, drug and billing information), semi-structured data (data generated from sensors and devices for monitoring patient behavior), and unstructured data (clinical letters, prescriptions, discharge summary, biomedical literature). Healthcare data serves multiple purposes (Palanisamy & Thirunavukarasu, 2019):

• Analysis of healthcare leads to important information for use by stakeholders for efficient clinical decision-making, population health management, and disease surveillance. 

• The development of healthcare frameworks using big data assists in personalized care and precision medicine. 

• Understanding patterns in data leads to novel healthcare solutions such as the patient-centric model and disease-centric model.

Promotion: Data Analytics: The Ultimate Beginner's Guide to Data Analytics Paperback – Import, 23 July 2019 by Edward Mize (Author)

   

Utilizing Healthcare Big Data

To achieve a purpose, healthcare data needs to be curated using domain specific tools and techniques. Complex, interdependent medical data needs to be simplified, interconnections between health features identified, and target attributes need to be selected for healthcare analytics (Palanisamy & Thirunavukarasu, 2019). Data streamed from devices must be integrated to feed into predictive models, medical image formats must be constructed and compressed. Big data tools are also useful in data enrichment activities. 

Redefining the characteristics of big data as “silo, security, and variety” in place of “volume, velocity, and variety” helps understand the context of big data in healthcare (Palanisamy & Thirunavukarasu, 2019). “Silos” refer to legacy databases containing public healthcare information in hospitals. “Security” refers to the care required to maintain healthcare data. “Variety” refers to the multiple formats of healthcare data including structured, unstructured, and semi-structured data.

Big Data Frameworks for Healthcare Data

Several big data frameworks have been proposed in research studies (Palanisamy & Thirunavukarasu, 2019):

• A framework consisting of data source layer, transformation layer, big data platform layer, and analytical layer takes internal and external data sources in multiple formats and extracts, transforms, and loads data using staging techniques (middleware and data warehousing). Further specific operations are performed by the Hadoop distributed file system (HDFS) using map-reduce. Finally, “querying, reporting, online analytical processing, and data mining” is performed in the analysis stage.

• Another patient-centric personalized healthcare framework used a collaborative filtering approach to using patient similarities to predict personalized disease risk. The system was designed to handle ICD codes.

• Another framework used vital signs extracted from accelerometers (as continuous time series data) to provide healthcare services. Motion data, respiration data, and ECG signals were used to feed into an interoperable system designed using Hadoop and Map-Reduce algorithm.

Promotion: Actionable Intelligence - A Guide to Delivering Business Results with Big Data Fast! Hardcover – Import, 24 October 2014 by KB Carter (Author)

   

In addition, several disease specific frameworks have been designed such as for AIDS, in policy enforcement, authentication and authorization (secure health systems), decision-support for practitioners, public health services, and drug design (Palanisamy & Thirunavukarasu, 2019).  

In effect big data frameworks are designed to meet specific healthcare objectives through the use of standard guidelines. Given the continuous data growth and evolution, integration is a primary means of aggregating data from multiple sources for transforming data, tracking procedures and metrics, and using business rules to build prescriptive and predictive models. Powerful, high-performance systems can query large volumes of data and transform it into “actionable knowledge” and identifying trends and deviations to improve public health and clinical health delivery. Insights gained from the data analysis decrease costs and improve care delivery. 

Keywords

healthcare data, security, population health, cost cutting, patient-centric, healthcare costing, improving care quality, variety, big data, big data frameworks, silo, personalized medicine

References

Palanisamy, V., & Thirunavukarasu, R. (2019). Implications of big data analytics in developing healthcare frameworks – A review. Journal of King Saud University - Computer and Information Sciences, 31(4), 415–425. https://doi.org/10.1016/j.jksuci.2017.12.007