Healthcare data analytics in B2B: The key to patient-centric healthcare ecosystem
The healthcare industry is on the cusp of a data-driven revolution. Healthcare data analytics, powered by advanced machine learning algorithms and vast patient datasets, is transforming patient care. For instance, Mayo Clinic’s Health Insights Program analyzes patient data using predictive analytics to identify the risk of developing certain diseases. This technology is integral to crafting preventative care programs, allowing patients sufficient opportunity to fight diseases. Predictive models like this are changing the healthcare landscape for the better.
In this blog, we will elucidate the crucial B2B applications of healthcare data analytics. We’ll explore their potential to innovate care delivery, streamline operations, and improve patient outcomes.
Predictive analytics: The third eye of healthcare
The ability to use historical and real-time data to spot patterns, and trends, for proactive decision-making is what makes predictive analytics such a hot topic in the healthcare landscape. Besides, it can have a profound impact on the $13 trillion industry.
Here’s how –
- Healthcare data analytics is uniquely qualified to predict deadly outbreaks such as the COVID-19 pandemic. If not prevent, being able to anticipate can at least help the healthcare industry minimize the impact of sudden infectious outbreaks.
- Predictive models take into account patient histories, genetics, and lifestyle factors to create a tailored and precise treatment.
- Predictive analytics is capable of reducing operational costs across hospitals by anticipating patient volumes, optimizing staffing, and reducing wait times. In addition to curbed costs, this can also help doctors level up patient satisfaction.
Read more: 5 breakthroughs powered by AI and Big Data in healthcare
Data-driven B2B: Why should B2C have all the fun?
In the B2B landscape, healthcare data analytics is fostering collaboration between stakeholders, including pharmaceutical companies, insurers, and technology providers. These partnerships unlock significant opportunities:
Drug development
Predictive models accelerate drug discovery by identifying potential candidates and assessing risks earlier in the development process. Recently, precision medicine innovator, Tempus announced plans to use multimodal real-world datasets along with biological model systems to empower Takeda’s oncology R&D efforts. Tempus’ analytics platform will help advance Takeda’s cancer therapeutics pipeline, including small molecules, antibody-drug conjugates (ADCs), bispecifics, and gamma delta T-cell therapies.
Resource optimization
To maximize the efficiency in a hospital, it is essential to allocate resources adequately. Predictive models have emerged as a blessing, allowing healthcare facilities to predict the need for services and optimize resource allocation. Smart tools are able to analyze the volume of incoming patients. These insights help hospitals make provisions on staffing to ensure optimum usage of beds and prevent overcrowding of wards. Ultimately, data analytics results in lower operational costs and higher quality care.Â
Supply chain optimization
Pharmaceutical supply chains are often complex and prone to disruption. Using healthcare data analytics, companies can accurately forecast demand, reduce waste, and prevent shortages. Besides demand forecasting, analytics can also improve inventory management by tracking stock levels, and ensuring reordering processes are optimized. It also enhances distribution efficiency so that life-saving drugs reach the right patient at the right time. For example, Pfizer, and Johnson & Johnson used predictive models during the COVID-19 pandemic to track vaccine deliveries. They were able to maintain steady supplies, ensure timely delivery, and mitigate potential shortages.
Insurance
Automating healthcare transactions can be very beneficial. Technology-enabled claim enquiries, benefit verification, and referrals have saved the healthcare industry $187 billion annually. Insurers can leverage analytics to improve underwriting processes, reduce fraud, and offer personalized policies based on predictive risk assessments. In fact, the Centers for Medicaid & Medicare services report $210 million worth savings in fraud-related losses using healthcare data analysis.
Unlock collaborations: The engine of healthcare data analytics
The success of predictive analytics hinges on robust data-sharing mechanisms. Ciox Health® offers Oscar Health, an insurance provider complete access to their Datavant Switchboard as a part of their partnership. This allows Oscar to efficiently request, gain, and deliver clinical data digitally. Not only does this collaboration improve access to medical records, it also reduces the turnaround time. Partnerships are catalytic in nature, unlocking opportunities for innovation and advancements in healthcare data analytics. Over the next few decades traditional healthcare practices might just become obsolete, and AI, ML, and analytics could change the face of patient care.
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