Healthcare Analytics: A Step Forward

Manage with data driven insights, not just hunches

The healthcare industry is going through a transformational change. From a reactive schedule at last minute and response at critical hour, healthcare service providers want to move to systems and processes that help them to predict, plan early and execute efficiently for any medical requirement.

Healthcare providers are exploring IT innovations like cloud, Big Data, Mobility, and newer medical procedures to achieve a quick recovery and safe patient journey. With this,they are attempting to ensure streamliningof operations, and maximizing their efficiency.

With some interesting innovations in Healthcare Analytics, data driven decision tools are helping service providers to make informed and quality decisions. In its simplest form healthcare analytics dealing with extensive use of data, statistical and qualitative analysis enables better decision making tools. The tools ranging from simple multivariate statistical methods to complex AI algorithms have enhanced the performance of hospitals.

Besides Healthcare service providers are using analytics to forecast the probability of occurrence of season flu, additional bed requirements, extended timings of anesthetists based on accident history of the city, or nursing superintendent holidays.

Using data driven models to analyze chronic illness, associate causes, diagnose the right symptoms and prescribe impactive medicine,healthcare analytics is playing a major role in preventing the illness. Healthcare analytics is also used to personalize the treatment. Some of the combination of drugs can lead to adverse effects.

Hence, healthcare service providers are using analytics to test the drugs for their efficacy, build analytical models to identify the likely side effects thereby reducing human suffering and costly insurance, and helping to develop safe patient friendly procedures.

The giant strides of healthcare service providers are enabling to employ analytical tools to design the right capacity of Operation theatres, or identify the right number of scanning machine, or pricing of services to meet service requests across customer segments, or design of loyalty plans and improve care and operational efficiency.

Further considering CDSS, to improve patient safety norms and reduce medical errors, clinical decision support system acts as an intellect for the implementation of electronic health record.This not only enhances patient satisfaction but also ensures easy access of physician profiles and clinical performance.

For decision making tasks such as determining and diagnosis of patient data, clinical decision support system acts as a helpful hand for physicians and healthcare professionals. In fact, one can say CDSS as a primary topic of artificial intelligence in healthcare domain that acts as bridge between healthcare observation and health knowledge which persuades health choices by clinicians for improved healthcare.

In order to enhance prevention and improve disease management system, CDSS helps in facilitating every point of care with available amount of data.

From surgical analytics to service line profitability to revenue cycle management analytics CDSS can provide critical insights in meeting organizational goals.It not only focuses on optimizing supply chain and human capital management but also increasingly look forward to improve revenue by reducing fraud and misuse of financial resources.

Healthcare organizations have to now look at their data and technology investments in new and innovative ways to gain competitive advantage.In order to improve utilization and risk management, there requires a creation of system that provides framework for the world class operational efficiency.