Using predictive analytics and big data to improve care at the community pharmacies
Predictive analysis is a process of using data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The use of predictive analysis has become increasingly popular in the business world, especially in retail and healthcare industries. In recent years, community pharmacies have started to embrace predictive analysis to improve their operations and enhance patient care.
Community pharmacies play a critical role in the healthcare system by providing medication and advice to patients. According to the National Community Pharmacists Association, there are approximately 22,000 independent community pharmacies in the United States, and they serve as the first point of contact for patients seeking medication and health advice. These pharmacies also provide medication management services, including medication adherence counseling and medication therapy management. The use of predictive analysis can help community pharmacies to optimize their operations and provide better patient care.
One of the key benefits of using predictive analysis in community pharmacies is improving medication adherence. Medication non-adherence is a significant problem in healthcare, and it can lead to poor health outcomes and increased healthcare costs. According to the Centers for Disease Control and Prevention, non-adherence to medication regimens accounts for approximately 125,000 deaths annually in the United States. Predictive analysis can help community pharmacies to identify patients who are at risk of non-adherence and provide proactive interventions to improve adherence.
Predictive analysis can be used to analyze patient data, such as medication history, medical conditions, and demographic information, to predict the likelihood of medication non-adherence. By identifying patients who are at risk of non-adherence, community pharmacies can develop targeted interventions to improve adherence. For example, pharmacies can use automated medication reminder systems to send alerts to patients when it is time to take their medication. Pharmacies can also use medication synchronization programs to align medication refills and reduce the frequency of pharmacy visits for patients.
Another way predictive analysis can be used in community pharmacies is to optimize inventory management. Pharmacies need to maintain an inventory of medications to meet patient needs, but overstocking can lead to waste and increased costs. Predictive analysis can be used to analyze historical data and predict future demand for medications. By accurately predicting medication demand, community pharmacies can optimize their inventory management and reduce the risk of overstocking or understocking.
Predictive analysis can also be used to identify drug interactions and adverse drug events. Community pharmacies often fill multiple prescriptions for the same patient, and the risk of drug interactions and adverse drug events is high. Predictive analysis can be used to analyze medication history and identify potential drug interactions and adverse drug events. By identifying potential issues before they occur, community pharmacies can take proactive measures to prevent negative health outcomes.
Additionally, predictive analysis can be used to identify patients who are at risk of hospital readmission. Hospital readmissions are a significant problem in healthcare, and they can lead to increased healthcare costs and poor health outcomes for patients. Predictive analysis can be used to analyze patient data, such as medical history, demographic information, and social determinants of health, to predict the likelihood of hospital readmission. By identifying patients who are at risk of readmission, community pharmacies can provide targeted interventions to prevent readmission.
One of the challenges of using predictive analysis in community pharmacies is data collection and analysis. Community pharmacies may not have access to the necessary data or resources to implement predictive analysis. Additionally, community pharmacies may not have the expertise to analyze data and implement predictive algorithms. However, there are companies and organizations that specialize in healthcare analytics and can provide support to community pharmacies.
Another challenge is the cost of implementing predictive analysis. Implementing predictive analysis requires an investment in technology, software, and personnel. However, the benefits of predictive analysis, such as improved medication adherence and inventory management, can outweigh the costs in the long run.
Predictive analysis is a powerful tool that can be used to improve care and streamline health care process at pharmacies. If you would like to learn more, contact us at 888-442-8348 to speak to our experts.