Analyze and compare performance of clinics to ensure adherence of compliance standards
Healthcare providers have to submit reports to the government with necessary details of the patients that are treated within their hospitals. For data managers, comparing data collected from various clinics to make sure that they abide by the compliance standards is overwhelming. Machine learning helps healthcare firms to track, compare, and maintain the internal data with the standard datasets provided by the government which eases the task of data managers.
With the ever-changing compliance policies, it becomes difficult to get insights from the respective hospital sites to check if they are adhering to compliance policies. With machine learning and big data in place, it becomes easy to collate data collected from various sites using different methods as well as data that is not properly formatted.
The disparate and unstructured data from external sources makes it difficult to generate analytical reports for statistical calculations and apply complex application of business rules. Machine learning enables analysis of greater amounts of data, and supports greater variability and complexity within the data. Most importantly, machine learning algorithms are adaptive of ever-changing parameters and data points.
Machine learning capabilities of SIA, Softweb Intelligence and Analytics platform, helps healthcare providers to efficiently analyze data from various healthcare data points like administrative data and electronic medical records across the entire clinical network in order to check if certain clinics or departments are meeting the compliance standards. It benefits healthcare providers by reducing dependency, latency, and reusability. SIA enables them to quickly search across various reports for parameters that indicate issues with compliance adherence.