Healthcare Analytics are the set of activities or actions undertaken as a result of data collected from different areas within healthcare. These areas are four in number and they are Claims and cost data, pharmaceutical and research development (R&D) data, clinical data (electronic medical records) and patient behavior and sentiment data(i.e patient behaviors and preferences). https://www.healthcatalyst.com/healthcare-analytics-best-practices
Healthcare Analytics focuses on the examination of patterns among various healthcare data, Big Data. A proper analysis of this big data provides healthcare researchers and practitioners with a comprehensive clinical, financial, fraud, HR and supply chain analysis. Because of the magnitude of this data and the different sources from which it’s derived, it can get cumbersome and difficult to manage the data. Hence some best practices have been developed outlines the most effective ways to approach healthcare analytics.
- Provide Analysts with a data warehouse
Organizations are advised to establish an Enterprise Data Warehouse otherwise known as EDW instead of handling data manually. The EDW becomes a central location for analysts to access all data across the entire healthcare system.
- Provide analysts with full access to a testing environment
After an enterprise data warehouse is established, analysts should be given full access to this data. Analysts can organize the data, rebuild sets and improve on it in order to further advance their work.
- Focus on skill sets
Healthcare analytics systems should be built for sustainability by focusing on skill sets. A roadmap is then created which allows organizations to gradually build on the required skill set of the organization. This way, risk and dependency on outside resources will be minimized. Future resource needs should also be planned for to sustain future organization needs and growth.
- Provide analysts with data discovery tools
Business Intelligence tools (BI) make it possible for analysts to drill into data and find trends and meaningful correlations. The right data discovery tool should enable analysts to build intertwined, insightful reports that lead to system improvements.
- Plan Healthcare Analytics with competitive landscape in mind
Organizations should take the competition and evolving strategic environment into account. This means building with long term goals in mind and not just short-term wins. Organizations should also ensure that the analytics frameworks factors in data sets that would be needed in the future.
- Focus on Data quality rather than data quantity
The relevance and quality of information are more important than the quantity of data aggregated in the system. Organizations shouldn’t focus on the amount of data but instead on what information it translates into and the possible trends in healthcare and outcomes. Too much 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.
- Build with the end in mind
Organizations shouldn’t rush to implement technology solutions when addressing operational needs. Technology should be installed as an enabler of the overall operation framework and its needs. Key long and short term strategic goals and the desired operational outcomes should be outlined at the beginning before the framework is built. Ongoing competitive trends should also be factored into the planning.
- Start small and build for scalability
Health organizations are advised to not build complex systems right off the bat. Information produced from an analytics platform might identify a problem, but an established operational framework is needed to solve the problem. Instead, organizations should start with a structured proof of concept that builds the framework and aims at cashing in on low hanging fruit and quick wins. They should recognize that the analytics roadmap is not a one-time implementation and also consider the agile process for scalability that allows for interactive input and expansion.