Why is Healthcare Data Analytics Needed?

The data analytics market in the healthcare space has only increased over the last few years. Considering the rising costs of medical treatments globally, a proper body of knowledge was needed to reduce the costs at the business-level as well as the professional-level. McKinsey, in one of its reports, states that healthcare expenses constitute 17.6 percent of the GDP in the USA, which is approximately US$600 billion, more than what is the set benchmark for the ideal size of population in the country. This is a serious indicator of bigger trouble. Hence, the usage of healthcare data analytics is being promoted these days.

Healthcare expenses constitute 17.6 percent of the GDP in the USA, which is approximately US$600 billion

Healthcare data analytics aims at reducing the cost of healthcare operations and processes. Hence, the treatment cost for patients will gradually go down. Not only this, healthcare data analytics has opened the doors to a plethora of job opportunities for qualified and skilled data analytics professionals. These professionals come with data-driven minds and strategic thinking, which is the need of the hour for the global healthcare industry.

Strategies For Data Analytics In Healthcare:

The initial step of leveraging data to achieve the three growth drivers is to break data silos, transform raw, unstructured data into structured and formatted databases, and organize it at one central data repository for further analysis. These tasks require the expertise of data processing companies, with the domain knowledge of the healthcare industry to ensure regulatory compliance. Once performed successfully, the transformed data and generated insights can be used as fuel to drive the following strategies:

360° Patient View:

Imagine having an eagle's eye view of your patient's journey, from appointment scheduling and test results to post-treatment rehabilitation and medical history. This holistic view can be gained by collecting data from disparate sources such as electronic health records (EHRs), patient's medical files, and patient portals, integrating them, and transforming them into a structured format. Through this data processing, healthcare providers can better understand a patient's diagnosis, prognosis, health history, and engagement patterns.

Operational Analytics:

Healthcare is a complex model with various branches, creating volumes of data. It is a storehouse of valuable insights into improving operational efficiency. However, generating actionable insights from raw, siloed data requires the expertise of a data processing consultant. These insights are a stepping stone to detecting bottlenecks in areas like resource allocation, care delivery, and patient management while streamlining end-to-end operations for reduced patient wait time and optimized resource utilization.

Hyper-Personalized Patient Engagement:

Avoid treating all patients the same way. Each patient is unique in their journey, which requires unique communication. Harness patient data like diagnoses and treatment plans from their medical records and EHRs to customize outreach and interactions. This could involve offering custom wellness programs, educational materials to support them during their recovery phase, or targeted reminders for medications, tests, or appointments. Such personalized engagement improves patient relationships and adherence to the treatment plan.

Predictive Health Modeling:

By integrating, processing, and analyzing various data points for a specific populace, future health trends, the populace's health needs, and even potential outbreaks can be predicted. These predictions facilitate healthcare providers, government agencies, and NGOs to follow a proactive approach toward resource planning and care delivery. Imagine being able to pinpoint individuals with a higher risk of chronic disease and intervene early with physician-approved preventive measures.