1. Accurate Needs Assessment
For successful stewardship, it’s essential to assess the population’s health needs before allocating limited resources. One of the best ways to do this is by aggregating data. When providers and state health department officials link data from healthcare exchanges and socio-economic data, predictive models may be used to forecast future health events. Large datasets may help to divide up an entire population by risk metrics.
Models can use machine learning or appropriate statistical analyses to reveal the connection between persons’ individual characteristics and risk measures. Examples of individual characteristics include gender, age, and diagnoses, while risk measures may consist of the risk of admission to hospital, patient complexity, and the possibility of adverse events.
2. Virtual Registries Can Provide Relevant Knowledge
Health authorities can develop virtual registries for chronic diseases through data extraction from different sources. With effective healthcare stewardship solutions, healthcare providers and state authorities can develop systems for extracting valuable data from electronic health records (EHRs), primary care, hospital admissions, and pharmacies.
While traditional disease registries are expensive to maintain, connecting datasets from various sources can be a more cost-effective way to develop an information databank that will give relevant information for policy formulation and decision making.
The Center for Disease Control, for example, states that about 30 million people in the U.S. have diabetes. However, about 24% of them are undiagnosed. This is due to the high cost of nation-wide laboratory testing.
However, the cost of testing should not be a barrier to the successful identification of potential diabetic patients. Models that use data from EHRs have revealed that it is possible to predict, with high accuracy, the people who could have undiagnosed diabetes and make them a priority for lab testing.
3. Monitoring Quality of Care
Data extracted from frequently used systems like EHRs, electronic prescriptions, and insurance claims provide better monitoring of care quality. The effective use of analytics can turn the mass of data generated in EHRs into a valuable resource for tracking and improving the quality of service at different levels of the health system.
Data generated daily by clinicians can produce maps of variation in the delivery of healthcare. A good example is the Dartmouth Atlas of Healthcare. Although the information provided does not give full details about the causes of the variations, it serves as a take-off point for detailed quality reviews. After the assessment, there will be information to perform a proper deployment of health resources to areas with a greater need or lower quality of care to improve effectiveness and equity.
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