Organizations use data analytics to discover, interpret and predict data patterns. They use analytics to gain insights and support decision-making. For this purpose, different forms of analytics are used in different industries, and the healthcare industry is one among them. Here is an article on how healthcare data analytics is changing the industry.
1) Eliminating preventable patient harm using healthcare data analytics
Data analytics enables healthcare providers to avoid treatment mistakes. Some of the mistakes are preventable, which can be detected in the early stages itself. This helps to avoid inflicting unnecessary patient harm and post-op infections. For example, the analysis of patient re-admittance metrics helps to prevent avoidable harm.
2) Eradicating diseases
Healthcare data analytics solutions help governments to eradicate life-threatening diseases. For example, millions of children die from malaria in Africa. As a result, the Government of Zambia along with the global non-profit health organization, PATH (Program for Appropriate Technology in Health), has started the Visualize No Malaria project.
Community health workers report data from the field to the data analytics system. It helps to identify communities where the parasite is hiding. And the healthcare staff get ready with the insecticide-treated bed nets, medicines, and other life-saving supplies. The operational dashboards enable the healthcare personnel to know where and when to intervene, so the malaria cases come down.
3) Avoiding opioid abuse using healthcare data analytics
Around 130 Americans die every day from an opioid overdose, according to the Center for Disease Control (CDC). As a result, opioid overdose was the most common cause of accidental death in the USA, with road accidents taking the 2nd spot. Data analytics helps to solve this issue. Blue Cross Blue Shield is using healthcare data analytics solutions to analyze insurance data and pharmacy data. Consequently, the organization was able to identify 742 risk factors that relate to patients with a high risk of opioid abuse.
4) Evaluating physician performance
Healthcare data analytics provide new doors to evaluate the performance of healthcare practitioners. It is useful to gather feedback on the physicians along with the health data of patients. For example, the Ongoing Professional Practice designed by McKesson Corporation helps evaluate the physician performance by integrating observation data, complaints, patient outcomes, practice patterns, and resource usage. It enables to evaluate real-time physician performance, improve physician practices and deliver better patient care.
5) Reducing healthcare costs by finding high-risk health populations
Chronic disease treatment constitutes one of the largest healthcare costs in the industry. Predictive data analytics cuts down these costs by predicting the high-risk health populations. Consequently, early intervention helps to avoid the diseases. Such healthcare data analytics include gathering data related to medical history, comorbidities, socio-economic profiles, and demographics. And medical history also includes the family history of chronic conditions.
Data analytics modeling include socio-economic factors and environmental factors to predict the risk of chronic diseases. Also, the modeling includes accounting for the comorbidities. The healthcare providers effectively allocate resources, thus focusing more on high-risk populations early and avoid long-term systemic costs.
6) Minimizing human errors using healthcare data analytics
Human errors often lead to preventable health issues. For example, a physician may prescribe the wrong medication or the wrong dosage. This leads to unnecessary insurance and claims costs. The patient data and medication data are analyzed and corroborated to trigger alerts in case of unusual medications or dosages. Moreover, human errors are reduced, and unnecessary patient health concerns are neglected.