Healthcare Data Analytics: Tips and Best Practices
Healthcare companies are in possession of more patient data than ever before, all thanks to advanced technologies and record keeping mechanisms. Healthcare providers can use various data analytics tools to extract useful information and examine patterns among the data available to them. A proper data analysis would also provide healthcare researchers and practitioners with a comprehensive […]READ MORE >>
Healthcare companies are in possession of more patient data than ever before, all thanks to advanced technologies and record keeping mechanisms. Healthcare providers can use various data analytics tools to extract useful information and examine patterns among the data available to them. A proper data analysis would also provide healthcare researchers and practitioners with a comprehensive clinical, financial, fraud, HR and supply chain analysis. The magnitude of this data and the different sources from which it’s derived often makes it cumbersome and difficult to manage. Hence these best practices outline the most effective ways toapproach healthcare data analytics:
Focus on data quality rather than quantity
It is vital for healthcare providers to focus on the relevance and quality of information rather than the quantity of data aggregated in the system. Organizations must lay in-depth focus on what information the data translates into and the possible trends in healthcare and outcomes. The presence of a large amount of irrelevant data can hide insightful and useful information. By recognizing data characteristics organizations can build a meaningful data quality management framework and deploy the most appropriate data cleaning workflows.
Keep an eye on the competitive landscape
While using healthcare data to formulate strategic decisions, organizations must take into account the competition and evolving strategic environment. This also means formulating strategies based on long-term goals in mind rather than giving excessive importance to short-term wins. Organizations should also ensure that healthcare analytics frameworks help in the creation of data sets that would be useful in the future.
Data warehouse and access to the testing environment
It is ideal for healthcare organizations to establish an enterprise data warehouse also known as EDW instead of handling data manually. The EDW acts as a central location for analysts to access all data across the entire healthcare system. After an enterprise data warehouse is established, it is important to give analysts full access to this data. With the help of various data analytics tools, analysts can organize the data, rebuild sets, and improve on it in order to further advance their work.
Healthcare analytics systems must revolve around the idea of sustainability by focusing on skill sets. A roadmap is then developed, helping healthcare organizations to gradually build on the required skill set. This greatly helps in minimizing the risk and dependency on outside resources. It is essential for healthcare enterprises to plan future resource needs in order to sustain future organization needs and growth.
Build for scalability
It is advisable for healthcare companies to not build complex systems right off the bat. Information produced from a healthcare analytics platform might be successful in identifying a problem, but an established operational framework is needed to solve the problem. Organizations should start with a structured proof of concept that builds the framework and aims at cashing in on quick wins and low hanging fruits. It is critical to realize that analytics roadmap is not a one-time implementation.