Overlapping Relationship between Big Data, Machine Learning, and Value-Based Healthcare

Significant developments in healthcare, such as the increased need for coverage, escalating costs, and provider reimbursements, have triggered the need for adoption of big data technologies as a way of improving overall healthcare quality and efficiency. Recent advancements in data collection methodologies such as Electronic Health Records (EHR), infusion pump informatics, and physiological monitoring of data have created various opportunities for scientific discoveries in clinical application. Machine learning, which is termed as a way of discovering new knowledge, can play a huge role in aiding healthcare organizations to improve care delivery. This paper discusses the relationship between big data, machine learning, and value-based healthcare and their impact on improving the quality of health.

Big Data and Improved Healthcare Delivery

Annually, billions of dollars are invested in the collection of vast amounts of medical data. In the field of healthcare, big data is a term used to denote the immense details amassed from varied sources, such as pharmaceutical research, medical devices, EHRs, and genome sequencing. A study conducted by McKinsey Company indicated a very high financial impact of big data applications in the healthcare sector, amounting to over $300 billion annually in the U.S. alone (Zillner & Neururer, 179). As a way of aiding the collection of patient data, the National Institute of Health (NIH) flagged an ‘All for Us’ initiative.

Although continuously learning healthcare system is being advocated for by the Institute of Medicine as a way of closing the gap between clinical practice, patient and clinician significant, and scientific discovery, the promise of big data is yet to be realized to its full potential. Three main characteristics that distinguish big data from the traditional electronic health data include availability in extremely high volumes, derived from many sources, and span the entire health digital universe (Balgrosky, 4). Sources of big data in healthcare include payer records, smartphones, patient portals, generic databases, government agencies, wearable devices, EHR, and research studies.

Big data can be used to improve healthcare delivery in a couple of ways. For example, the technology can be used to diversify diagnostic services as it offers patients widened access to healthcare. Mobile Applications such as Triage are advising patients on their conditions using big data applications (Zillner & Neururer, 179). Big data can also be leveraged to enhance precision medicine by utilizing the available data to reduce medical errors. Shorter hospital stays and fewer admissions, all of which can be propelled by big data, can lead to reduced costs of healthcare.

Machine Learning and Improved Healthcare Delivery

Machine Learning (ML) in healthcare has made headlines in the recent past. Companies such as Google have developed algorithms that identify cancerous tumors (Wiens & Shenoy, 149). Similarly, Stanford has developed learning algorithms used in identification of skin cancer (Wiens & Shenoy, 152). As a result of the critical roles that technology has been playing inpatient care, machine learning is gaining acceptance in today’s healthcare industry. Machine learning is helping to sort out a wide range of situations in healthcare. Key areas that can harness the technologies of machine learning include analyzing thousands of medical data, suggesting outcomes, enhancing resource allocation, and providing timely risk scores.

Numerous machine learning applications are shaping healthcare delivery. For instance, ML applications are assisting physicians in the identification and subsequent diagnosis of ailments and diseases, which, for a long time, have been considered hard-to-diagnose. Such conditions range from initial stages of cancer to genetic disorders. Berg, a biopharma giant, is harnessing the use of Artificial Intelligence (AI) to develop treatments in therapeutic areas such as oncology (Wiens & Shenoy, 152). Drug manufacturing is another critical application of machine learning. Research and Development (R&D) technologies such as Project Hanover by Microsoft are using technology to personalize drug combinations for Acute Myeloid Leukemia (AML) (Balgrosky, 163). ML has also been instrumental in outbreak prediction, better radiotherapy, crowdsourced data collection, clinical trial and research, and ML-based behavioral modifications that improve healthcare delivery among patients.

Value-Based Healthcare Systems and Improved Healthcare Delivery

There is a significant link between the provision of value-based healthcare and medical technologies such as Artificial Intelligence, Machine Learning, and big data. Typically, value-based healthcare delivery systems use models in which care providers, such as physicians and hospitals, are remunerated based on patient health outcomes (Bozic & Wright, 1004). Mostly, caregivers are paid for helping patients to reduce the adverse effects of chronic diseases, promoting healthier lives, and improving patient’s general health. As opposed to the traditional capitated or fee-for-service models where providers of healthcare are paid based on the number of healthcare services rendered, value-based care systems measure outcomes against the cost of delivering these outcomes.

Value-based healthcare systems have a lot of benefits and contribute immensely to improved healthcare delivery. Such interests can be looked at from different angles. For instance, the advantages are extended to patients, providers, suppliers, society at large, and payers. Patients can spend less money for better health outcomes (Bozic & Wright, 1005). On the other hand, providers can benefit from higher patient satisfaction and improved care efficiencies. Moreover, payers can reduce and control risks. Since all these people are stakeholders in a sound healthcare system, the system is very critical in promoting healthcare delivery.

Quality, outcome, and value are three buzzwords that accompany healthcare systems across the world. In a world where patients are demanding more value for their money, providers have to integrate technology to gather data, analyze it, and use the information to improve care delivery. There is a direct correlation between improved healthcare delivery and techniques such as big data, Machine Learning (ML), and value-based healthcare systems.



Works Cited

Balgrosky, Jean A. Essentials of Health Information Systems and Technology. Jones & Bartlett Publishers, 2014.

Bozic, Kevin J., and James G. Wright. “Value-Based Healthcare and Orthopaedic Surgery: Editorial Comment.” Clinical Orthopaedics and Related Research® 470.4 (2012): 1004-1005.

Wiens, Jenna, and Erica S. Shenoy. “Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology.” Clinical Infectious Diseases 66.1 (2017): 149-153.

Zillner, Sonja, and Sabrina Neururer. “Big Data in the Health Sector.” New Horizons for a Data-Driven Economy. Springer, Cham, 2016. 179-194.