Today, almost every healthcare institution has some form of analytics in place. But, gathering data is the easy part; actually making actionable use of it is the real challenge.
Healthcare systems get data from many sources. This data can provide valuable insights, but much of it remains unused. Many healthcare providers recognize the importance of data-driven decision-making. However, they often lack the tools needed to use it.
To get the most out of their data, healthcare providers need to go beyond simply gathering it – they must apply it to drive work across every level of the organization. This begins with investing in an advanced data analytics solution that can support business-wide needs.
Hospitals also need to develop a culture that values data accessibility and empowers teams to utilize insights in their day-to-day decisions. Furthermore, healthcare providers need to identify their most significant challenges and collaborate with experts who understand precisely how to use data to address those concerns.
This blog explores how healthcare providers can apply data analytics to transform operations and make meaningful improvements.
Recruitment
Due to the high costs associated with recruiting and onboarding new employees, healthcare providers are turning to HR data analytics for retaining their workforce.
Resignations are inevitable, and hospitals should be tracking metrics related to current openings and weaknesses in their workflow to find where they need to direct their recruiting resources.
With advanced data analytics, healthcare institutions can measure their turnover and retention rates, gaining visibility into how many employees have left their company over a specified period. These numbers can then be drilled down by whether or not the turnover was voluntary, how well-performing those employees were, and how turnover rates compare between different departments. Understanding the situation in which an employee left is essential to addressing internal impacts and reducing future resignations.
To estimate the risk of future resignations, organizations can measure employee satisfaction. They can use surveys to find out if employees want to stay. It is also important to check compensation. This helps ensure employees are not leaving because of low pay. This data can be used to introduce new strategies or incentives for workforce retention.
Patient Engagement
Engaging with patients and developing a consistent line of communication between them and their providers is a difficult challenge for healthcare systems across the globe. Missed appointments and ignored treatment schedules can lead to poor care. They can also limit a healthcare system’s ability to use resources well.
With predictive analytics, hospitals can use a patient’s historical attendance data to forecast which patients pose a high risk for no-shows or poor adherence to arranged treatment plans.
These insights can be used by hospitals to establish a more frequent contact schedule with their patients. This predicting ability can also be applied in the care of patients diagnosed with chronic conditions. Using predictive risk scores derived from patient data, healthcare providers can design customized treatment plans that allow patients to independently track their treatment and engage with their health.
Moreover, a better understanding of patient behavior is more important for developing doctor-patient relationships. The stronger the relationship between a physician and their patient, the more likely they are to engage with their provider and adhere to regular screenings and follow-up appointments.
Resource Management
The COVID-19 pandemic showed us how destructive large-scale disruptions can be for healthcare supply chains, with hospitals suffering from shortages of ventilators, masks, gloves, and other PPE. Unfortunately, a lack of proper data analysis in healthcare systems can lead to big problems. Even small mistakes in resource allocation can have serious effects. In order to prepare for future disruptions, healthcare systems need more visibility into how and where their resources are being used.
With advanced data analytics, healthcare providers can predict and respond to potential shortages by finding weak links in their supply chain, which inventory is falling behind, and where. This real-time visibility allows health institutions to optimize their resource management and assign manpower to areas where there is more demand.
When there is a shortage, hospitals should be able to develop allocation policies on criteria such as urgency of need, probability, expected duration of successful treatment, and change in quality of life. But none of the data is accessible without an intelligent analytics platform that can communicate real-time triage data between nurses and doctors.
Implementing analytics in resource allocation also goes beyond just large-scale disruptions. With predictive analytics, healthcare providers can spot patterns in resource use based on seasons or regions. This helps them manage resources better and prepare for future needs. When it comes to daily operations, optimal resource management can also minimize unnecessary spending.
Conclusion
Healthcare systems have evolved far beyond conducting their analysis on pen and paper, but still, a wealth of valuable data remains unutilized. Despite advancements, financial leaks, operational inefficiencies, and poor patient outcomes remain major burdens for healthcare providers across the globe. However, continuing advances in healthcare technology and smart data utilization present a bright future for hospitals that are looking to drive meaningful change.
By turning insights into action, healthcare organizations can create a future where better outcomes, lower costs, and happier patients and providers become the norm rather than the exception.