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As the healthcare industry continues to move toward value-based care, hospitals are beginning to adjust to the change by including new ways to monitor and manage population health. Healthcare CIOs are adopting new software tools that can provide a clearer picture of the health of the populations their organizations serve to help achieve better patient outcomes.
EHR systems were designed to provide a centralized location for patient data, such as lab results, medical images and appointment notes. Since the data typically sits in the EHR with very little analysis or comparison to other patients' data, many physicians are seeking ways to view overall population health and address ongoing issues with patients with chronic diseases. This need for tools that can perform a deeper analysis of health information has pushed some EHR vendors to enhance their products with data mining and analytics capabilities.
Population health analytics consists of a tool that is capable of analyzing large sets of patient data to detect patients who may be considered high risk for complications. The analytics can also detect any trends or abnormalities and providing a platform that allows physicians to interact with and analyze their patient population data efficiently.
CIOs should consider the following steps to ensure the successful implementation of population health analytics.
Defining the initial goals and purpose
Healthcare organizations that plan to implement population health analytics must first understand what objectives they are trying to achieve, such as identifying high-risk patients or gaps in care, or improving health outcomes and cost savings. This first step provides clarity for all the participants in the project and a clear direction for the population health analytics initiative.
Gathering and connecting data to the new system
When it comes to population health, patient data is one of the most important aspects of the project. Without it, there can be no population health analytics initiative. Healthcare CIOs must ensure all the relevant data is available and accessible to the new analytics tool so that it can perform the analysis. This data may be stored in different systems within the hospital, such as EHRs, PACS and laboratory information systems.
Applying analytics tools to data visualization
When it comes to population health, an analytics tool's primary purpose is to highlight relevant insights from the data that the system is able to ingest. An adequate analytics tool should include these five core features in order to truly deliver value and meaningful insights:
- data analysis that uses advanced data processing capabilities with Python, R and other statistical tools;
- data visualization capabilities to deliver data summaries visually to end users;
- drill-down features that allow end users to dig deeper when they identify an issue that needs further investigation;
- data modeling and aggregation; and
- support for multiple data sources.
Guiding end users during the adoption phase
Healthcare professionals are constantly being asked to adopt new technology tools. But in order for population health analytics to succeed, a CIO will have to invest in providing end users with the appropriate training and processes to support the operationalization of the reports and insights collected.
Hospitals are realizing that adopting new modern analytics tools is a must to increase visibility into the health of their patient populations. Mining information that already resides in the EHR, as well as patient information from other sources like health registries and patient-generated health data, offers a great opportunity for new insights that can support patient and population improvement initiatives. Being able to execute the implementation of one or more population health analytics tools will support hospitals on their journey to improve patient outcomes and reduce costs.