Healthcare digitization has led to the widespread collection, sharing, analysis, and utilization of health data on multiple platforms, including wearable tech, mobile apps, medical devices, and AI-driven models. This shift has facilitated access to an expansive digital database of essential information for healthcare systems and other stakeholders. That plays a crucial role in improving healthcare outcomes, driving revenue growth for medical organizations, and establishing new partnerships for each healthcare software development company.
The collection of “big data” from various sources in healthcare has facilitated the prompt resolution of ongoing problems in routine patient care. Modern digital tools created by tech vendors like Exoft enable patients to easily schedule appointments online, replacing the need for extended wait times in doctors’ offices. By leveraging remote patient monitoring, healthcare professionals can track patient indicators and monitor treatment progress from anywhere, enabling them to make timely decisions even without direct physical examination.
Now, digital healthcare transformation is expanded by data scientists and medical staff to improve population health strategies, reshape the healthcare business sector, and inform critical organizational decisions. The industry’s key stakeholders, like healthcare providers, payers, and policymakers, are utilizing predictive data analytics in patient-centric data ecosystems to enhance value on a broader scale. But what are the future use cases of big data analytics in healthcare? Let’s check that.
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Expanding Population Health Efforts
Healthcare providers can use predictive modeling to analyze trends in the transmission of infectious diseases and pinpoint areas that may require more robust control measures. By doing so, medical organizations can proactively develop effective strategies for managing outbreaks, even in areas where the disease has not spread.
For instance, the researchers already used big data to construct a model that examined the transmission of COVID-19 in various locations. They collected extensive datasets from search engines, social media, contact tracing applications, and others to locate individuals who had come into contact with the virus. Additionally, they traced the infection’s source and identified areas where the virus was likely to spread, and new outbreaks might emerge.
Besides, another team of researchers utilized healthcare analytics to identify hospitalized patients at risk of heart failure and septic shock. The model used large data volumes to classify patients due to their risk levels. That ensured healthcare resources were directed towards those most susceptible to these conditions, even before the onset of symptoms.
Thus, healthcare providers will use predictive clinical analytics to pinpoint patients at risk of decline. With the AI-based model, they will analyze large sets of health records and other data sources to identify patients who may need emergency care. That will enable clinicians to prioritize care and promptly assist those who require it the most.
One key area of focus for big data analytics in the future is the integration of claims data, engagement data, and solution data. By combining these datasets, healthcare providers and payers can gain insight into managing chronic diseases and determine which aspects of patient care work well and which need improvement. This information can then be used to allocate resources and make necessary adjustments to enhance patient care.
Promoting Personalized Medicine
In recent years, personalized medicine has emerged as a new horizon in healthcare and big data analytics. The developments in genomic analytics and data science have generated new datasets that have transformed how clinical care providers administer treatments and diagnose illnesses.
This application of big data in healthcare relies on information to achieve precision in treatment. Patients with the same type of cancer are no longer treated uniformly. With the availability of vast amounts of genomic metadata and the decreasing cost of genetic testing, clinicians can identify genetic variations that require personalized treatment.
For instance, a particular drug may only work effectively in breast cancer patients with a particular gene coding for a receptor. Patients without that gene expression will not benefit from the medication. However, gathering and analyzing such large amounts of health data is essential to achieving this level of precision in healthcare.
Personalized medicine uses data from different sources, like medical care, social factors, etc. Experts are working to enhance these data sources’ quality, quantity, and diversity and integrate them into innovative tools and ecosystems. The goal is to extend precision medicine to a broader range of populations and diseases.
Reducing Healthcare Costs
Multiple organizations have significantly reduced healthcare costs through predictive data models. Personalized medicine will be crucial in eliminating unnecessary treatments, admissions, and diagnostics. With the help of vast sets of genomic data utilized for personalized treatment, patients will only get treatments for their specific conditions.
By utilizing predictive models, healthcare companies can lower operational costs by concentrating on patients at risk of non-compliance with treatment and complications. Data analytics also enables employers to identify individuals at risk of chronic conditions and offer them proactive and timely care, staying ahead of the curve. That will reduce the need for unnecessary surgeries and healthcare complications that would cost millions of dollars.
Data analytics can also assist healthcare providers in accurately predicting a patient’s length of stay and readmission rates. That helps providers plan and allocate staff more effectively, ensuring patients receive timely and precise care. It also enables them to identify patients at high risk of readmission and take proactive steps to reduce the associated hospital costs.
Thus, employers must update their corporate wellness strategies to address the health conditions and statistics that can be easily improved. For instance, if the readmission rates for depression and anxiety attacks are increasing, employers should enhance mental health support in the workplace and re-evaluate employee assistance programs to alleviate mental health problems among their employees.
The Feature of Big Data in Healthcare
Digital transformation is sweeping across various sectors, and healthcare is also undergoing a significant shift. With the outbreak of the COVID-19 pandemic, adopting digital techs in healthcare has accelerated, prompting many medical organizations to collaborate with data scientists to develop digital models and tools that can transform the healthcare landscape.
Ultimately, key industry stakeholders, such as healthcare providers, payers, and insurers, are leveraging the abundant healthcare data to reshape digital health by enhancing patient outcomes and cutting healthcare costs simultaneously.
