Big data healthcare utilizes large and complex sets of electronic healthcare data and applies advanced analytics techniques like machine learning and artificial intelligence to gain useful insights. It helps in improving population health management, precision medicine, drug discovery and personalized treatment.
The big data healthcare market is witnessing high growth owing to increasing adoption of artificial intelligence. AI plays a vital role in managing large volumes of healthcare data generated on a daily basis. It helps in predictive analytics by identifying patterns and making recommendations for improving clinical outcomes. Furthermore, growing focus on personalized healthcare is also fueling market growth. With the help of big data, individual patient profiles and medical histories can be analyzed to develop customized treatment plans. This is expected to significantly boost market revenue over the forecast period.
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Big Data Healthcare Market – Major Drivers, Restraints, Opportunity and Trends
Increasing Adoption of Electronic Health Records and Rise in Chronic Diseases is Fueling Market Growth
The adoption of electronic health records has increased exponentially over the past few years. EHRs generate huge amounts of complex medical data on a daily basis from diagnosis, treatment, pharmacological history, clinical trials, medical imaging and more. Analyzing this big data helps providers gain insights to improve quality of care, reduce medical errors and streamline operations. Many countries have also introduced initiatives and incentives to promote EHR adoption. With growing chronic disease burden worldwide, healthcare data is growing at an unprecedented rate which is driving demand for big data analytics solutions.
Growing Focus on Personalized Healthcare is Accelerating Market Growth
With advancement in genomics and rise of precision medicine, big data analytics is playing a major role in facilitating personalized and predictive care. Leveraging insights from large patient datasets help understand individual patient’s specific health risks, responsiveness to various treatments and tailor care accordingly. This is improving patient outcomes while reducing costs. Big data coupled with artificial intelligence is enabling emergence of “precision populations” by allowing providers identify high-risk patient cohorts for preventive intervention programs. The shift towards value-based care is also spurring healthcare organizations adoption of big data analytics to improve quality and lower costs.
Data Security and Privacy Concerns Pose Significant Challenge
One of the major challenges hampering faster adoption of big data in healthcare is security and privacy concerns around sensitive patient data. With increased instances of healthcare data breaches in recent times, patients as well as providers have significant anxieties around unauthorized access and sharing of confidential medical records. Addressing concerns around data ownership, consent management and compliance with stringent regulations continues to be a complex undertaking. High costs and skilled workforce required for secure storage, processing and analysis of healthcare big data on premises or cloud also poses operational and budgetary challenges for smaller providers.
Rise of Digital Health Startups Opening New Market Avenues
The digital health industry is witnessing a rapid rise of new startups developing innovative big data and AI-powered solutions targeting various aspects of care delivery and patient engagement. These include predictive analytics platforms for risk adjustment and population health management, clinical decision support tools, virtual health assistants, personalized care management platforms and more. Partnerships between digital health firms and traditional providers are opening up new revenue streams for monetizing big data assets. Venture funding in this space has grown exponentially over the past few years which is attracting more entrepreneurs to enter this promising market.
Adoption of Cloud and Cognitive Computing is Transforming Market Dynamics
With vast amounts of healthcare data being generated on a daily basis, traditional on-premise infrastructure is unable to keep pace with storage and analytic requirements. This is driving significant migration of big data workloads to public and hybrid cloud models which provide limitless scalable storage and computing capabilities. Advanced technologies like cognitive computing, machine learning and deep learning are enabling the next generation of clinician decision aids and automated diagnostics tools powered by huge healthcare datasets. The industry is poised to witness tectonic shift with integration of big data, AI, IoT and precision medicine over the next decade.
- Source: Coherent Market Insights, Public sources, Desk research
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