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Across different hospital networks and healthcare entities, several groups have successfully built population health analytics initiatives to improve the quality of patient care and identify at risk populations. Starting such an initiative requires a set of steps that ensure the data analysis is meaningful and insightful.
However, it takes more than just the analytics tools and data to ensure the success of a population health analytics initiative. In healthcare, the challenges many organizations face are incomplete data, inability to track patient progress across the care continuum and the inability for providers to actively use the analytics tools at hand. For those looking to engage in this journey, it is important to know what the key areas are that are crucial to the success of a population health analytics initiative.
Data access across the network and beyond the hospital systems
It is almost the norm to have a patient's data spread across several EHRs in different health systems. Whether the patient's primary care physician is independent or part of a hospital network, the fact remains that the relevant data that is needed in a patient registry is likely stored in multiple EHR systems.
A population health analytics initiative requires that a comprehensive patient record is accessible for review and analysis. This would require the healthcare organization hosting the system to negotiate and determine ways to access as much data as possible based on the metrics it needs to measure. This can lead the group to seek connectivity to a local health information exchange, accountable care organization or existing registry where the information is available. This is considered one of the most critical steps toward succeeding in a population health analytics initiative, and the reason why hospital sponsored population health management initiatives are more likely to succeed than those initiated by independent groups.
Population health requires physicians to formulate the metrics needed
An analytics initiative surrounding population health requires a clear definition of what clinicians want to measure. Clinicians must define the specific criteria in order for the data analyst to use those parameters to isolate, group and monitor those individuals, whether it's high risk discharged patients or patients who present a high risk for heart failure. An investment of time and clinical resources becomes a must in this step as it is the foundational piece for the population being monitored and analyzed.
Define data gaps and create mechanisms to capture missing data
Despite the availability of patient data from multiple clinical departments in a hospital, physicians may find themselves missing key information that could provide additional insights into the patient's condition. When information is not being collected inside the hospital where it can be integrated, then the group building the system is left with one option: to capture the information manually. This may require the hospital to develop additional clinical forms or fields that can be used during the care episode to capture the additional needed information.
Today's EHR systems offer the flexibility to create additional fields within flowsheets and clinical forms, and most physicians are able to collect new data from within the EHR. There are other use cases in which a hospital requires patient generated information from mobile apps or online web portals.
Use the right analytics tools to aid clinical adoption
Data analytics tools have not always been favored by clinicians. Some have been labeled as too complex, while others have been cast as just flashy charts. Having the right data visualization tool that is easy to use and quickly delivers insights into the population health data can drive adoption up. It can also encourage a wider use of the platform beyond one population health initiative. It is equally important that the analytics platform offer more than just a great visualization tool, such as support for multiple data sources and different data standards.
Maintain change tracking to patient's scorecards
One aspect of designing a population health analytics initiative that must be taken into consideration is the ability to store or maintain a daily snapshot of patients' scorecards. This is not always an out of the box feature; the default behavior of some analytics platforms is to update a patient's scorecard with the latest data. However, that can overwrite the scores that were previously measured. Ensuring that daily scores are stored separately ensure that a snapshot is maintained whenever data changes occur.
Population health analytics is not limited to accessing electronic health records. Hospitals can leverage the use of billing information to identity certain patient populations. With the increasing focus on improving outcomes, as well as a push for the shared saving payment model, more hospitals are finding it valuable to initiate such projects to reduce readmission rates and prevent complications. The use of analytics tools will continue to spread in hospitals and, as adoption rises, demand for interoperability and data exchange will increase as well.
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