Proponents of value-based health care are growing their bets on the capabilities of predictive analytics and the underlying AI algorithms. The past few years have witnessed an enormous body of study on making clinical prediction models useful in improving the health care delivery across the continuum. The optimism that surrounds the performance of healthcare predictive algorithms in enriching the clinical decision making on one hand and the business bottom-line of the healthcare providers on the other.
Whether be it the massive gains in operational management of hospitals, effectiveness in managing at-risk cohort patients, or use in population health models, solution providers in the healthcare analytics market are witnessing the growing buoyancy.
Organizations Rapidly Adopting Solutions to Enrich the Healthcare Ecosystem
The use cases of healthcare predictive analytics have expanded swifter than imagined. The healthcare predictive analytics market has gained growth impetus from the growing application of healthcare predictive analytics for monitoring patients in ICUs and general wards. Hospitals are fervently deploying the solutions in ways more than one. Providers are leveraging the solutions in the healthcare predictive analytics market for planning care for at-risk patients once they are discharged and sent home. Another area where the prognostic models in predictive analytics are being tested in the management of cancer patients. The analytics promise better long-term health monitoring in oncology. An example is the demand for biomarker-based algorithm to diagnose ovarian cancer.
Not just the providers, caregivers are also garnering the gains as analytics tend to boost their decision-making abilities. The adoption of automated early warning scoring by healthcare providers and caregivers to reduce the frequency of adverse events is a case in point. The analytics that the predictive algorithms can churn out continues to raise the bar higher. Evidently, a few hospitals have been found that to be helpful in managing patients with cardiovascular risks. Another tangible benefit was found in its use in avoiding 30-day hospital readmissions.
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Healthcare Predictive Analytics Market Extend Horizon in Elderly Care and Acute Care
The strides made in AI and IoT are further contributing to the prospects of the healthcare predictive analytics market. The clinical potential of data obtained from healthcare wearables is the growing testimony of the vast potential. Healthcare providers globally are harnessing the capabilities for improving the quality of life of the elderly. As healthcare predictive analytics gets weaved into real-time or near real-time clinical interventions, the demand for solutions for acute care, and rehabilitation has raised the ante higher for players in the healthcare predictive analytics market.
Aside from their widespread scope of their offerings in clinical settings, players in the healthcare predictive analytics market are witnessing benefits of the predictive analytics to increase cost outcomes. The administrative staff and hospital facility managers have found predictive analytics solutions to be useful in equipment maintenance.
The growing trend of evidence-based clinical practice will continue to impart momentum in the healthcare predictive analytics market. Another area with promising opportunities is businesses leveraging prognostic analytics and big data in personalized medicine.
The role of healthcare predictive analytics in population health is another area with massive market possibilities. The changing demographics mainly on account of changing incomes in emerging economies and the rapidly aging population has made population health management an interesting area to explore for tech companies. For instance, actuarial mathematical modelling when combined with EHR data is being used to elevate the standard of patient care for elderly populations worldwide.
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Potential Technological and Regulatory Challenges Remain
Despite the profound potential healthcare predictive analytics holds, the growth dynamics of the market is fraught with some potential bottlenecks. The lack of clarity of regulations and the absence of industry standards are some of the glaring shortcomings which need to be addressed by players in the healthcare ecosystem. The algorithm performance and its reliability have emerged as things of concern for software providers in the healthcare predictive analytics market. One way to enrich the reliability is setting up of independent external validation and making the standards publicly available. The growing high-value use cases will help the healthcare predictive analytics market address the key challenges in its strides.