Top 5 Use Cases of Analytics in Healthcare
03/24/2022 by Chris St. Jeor Healthcare Analytics
Milk and cookies, rock ‘n’ roll, Batman and Robin: these pairs are nothing compared to the relationship between healthcare and analytics. In fact, the first recorded example of analytics being applied to health and nutrition occurred in the first century. Paid clinical trials began as early as the 1600s. In recent years, however, both healthcare and analytics have increased in complexity.
Fortunately, analytics has become an integral part of the entire healthcare industry, speeding up decision making, better aligning valuable resources, and improving the overall patient experience. The rest of this article will highlight 5 real-world examples of analytics playing a fundamental role in the healthcare industry:
Perhaps the simplest, but most widely adopted use of analytics in health care, are medical claim KPI dashboards. Health care leadership teams depend on recurring monthly, weekly, and daily dashboards to help drive their decision making. These dashboards highlight everything from seasonal flu trends to daily inpatient bed inventory. Health care organizations and insurers across the world depend on data scientists to analyze their treasure trove of medical records and highlight their past, present, and future trends, needs, and obstacles.
Medical claims fraud, waste, and abuse are challenging for hospitals and insurers to manage. CMS estimates that it spent more than $86 billion on fraudulent payments in 2020. Examples of medical fraud range from surgeons manipulating records to reflect work that was not done in order to increase reimbursements, to patients “shopping” for providers to acquire prescriptions they do not need. Through outlier detection and prediction models, data scientists can use claims data to determine areas of potential fraud. By identifying small clusters of outlier claims at the provider and member level, auditors can single out which claims to focus on and identify the parties participating in the fraudulent activity.
Inpatient visits, especially inpatient readmissions, are often preventable high-cost events for health care providers and patients. When a patient is admitted to an inpatient facility, analysts use the patient’s medical history and other discharge information to create predictive models to identify patients at risk of subsequent readmission. Hospitals can then use the output from these models to determine which flags are causing a patient to be high risk for a readmission. These flags can then be used to create preventative care strategies for different patient types and help the hospital decrease their readmission rates.
Hospitals have a difficult time managing the number of available beds across their units. In order to efficiently manage their bed utilization and forecast demand, hospitals enlist the help of data scientists to both predict future demand of hospital beds, and predict when the occupied beds will become available.
Comorbidities are a leading indicator of a patient’s general health. Acquiring multiple comorbidities can have devastating impacts on a patient’s health and cost of care. Hospitals and insurers use data mining and machine learning to identify which combinations of comorbidities have the greatest impact on a patient’s health and to identify which patients are at the greatest risk of developing those combinations. Using this information, providers and insurers can create preventative care strategies tailored to their at-risk population to further manage the quality of care their patients receive and mitigate their risk of further complications.
While Zencos may not date back to the first century, with the first recorded union of analytics and health care, we do have a strong history of helping healthcare providers and payers apply analytics to their decision-making process. To learn more about the work Zencos is doing to help those in the healthcare industry become more data driven, please reach out. We would love to continue the conversation.