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Data Analytics for healthcare

Importance of Data Analytics in Healthcare

In recent times, the healthcare marketplace has become increasingly competitive and complex. A trend that is likely to continue in the foreseeable future. Healthcare data management is an important tool for breaking down the complexity of the industry. This in turn helps the healthcare organizations treat their patients more effectively. The ability to provide patients with holistic and personalised treatments enhances health outcomes. Organizations are getting sufficient data to help them understand what the patient needs are.

The next logical step would be to develop efficient tools and methods to create value from the data. This in turn can help the organization make informed decisions leading to improved quality in healthcare.

Data analytics provides a bigger picture of the patient’s condition, which ensures precision-driven care and treatment. This eventually leads to end-to-end process optimization and increased competitiveness.

For a Multi-functional Medical Diagnostic Solutions Provider in India, Tibil created Omni Health Monitoring System (OHMS), a state-of-the-art product that combines IOT, mobile delivery, data analytics, CRM, and social media to enable real-time, complete, customized, 24/7 patient engagement anywhere in the world.

Disease Surveillance and Preventative Management

Healthcare analysts work diligently on the data that is provided to them. Hence, they scour both structured and unstructured data sources, including the data that is readily available on non-traditional channels like social media messages, text messages and the like to discern any patterns. They convert all the information into actionable insights and work towards achieving better health outcomes.

Data analytics in healthcare

The proliferation of mobile devices has really helped in this regard too. It helps the analysts understand the path of infectious diseases (through the GPS coordinates obtained from the cell phone). This was first used during the Ebola outbreak in West Africa and enabled the authorities to take preventive measures. WeChat has played a huge role in containing the Corona outbreak in China. Cell phone mobility data can help Analysts understand not just present cases of the outbreak and infectious diseases, but it will also shed light on how diseases could spread in the future.

Data analytics in healthcare

Doctor to Patient Ratio

Physicians have traditionally used their training, experience and judgment to arrive at diagnosis and treatment decisions. Now they can supplement their expertise with data. While this is a huge opportunity the challenge right now is that the growth in data is way too fast and complex. Studies suggest that digitization in healthcare is currently responsible for 48% per year growth in data, compared to the average of 40% across all other industries. Without sophisticated analytical tools, unstructured data would place a massive cognitive load on physicians.

Financial Risk Management

Electronic Healthcare Records (HER) have traditionally been maintained by capital intensive legacy systems. Today however cost-efficient technology – Cloud, SaaS and IaaS, etc. – are available to provide economical and scalable tools into the hands of health care providers. More importantly, the technology is also driving greater transparency, making it possible for payors and regulators to use EHRs for compliance. The growing access to reliable data has already encouraged around 70% of payors in the US to move to outcome-based plans.For patients, analytics in the health care sector can be a massive benefit. Insurers are offering plans that are specific to their conditions such as plans for infectious diseases, respiratory and cardiac issues etc.

Healthcare analytics

Artificial Intelligence is the key to financial risk management. As many healthcare provides want to make a shift from the fee-for-service to the performance contract model, one of the biggest financial challenges here lies in the fact that it takes quite a lot of time in determining patient outcomes and to decide the payment. Other challenges include lower reimbursements, unpaid patient bills and underused billing and under-utilized record keeping technology. Predictive analytics can help the cash flow for hospitals by determining the accounts that demand payment, and also predict which payments are likely to remain unpaid in the future.

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