Clinical efficiency is vital to cutting costs for healthcare organizations and improving outcomes for patients, especially when implementing value-based care. But knowing where and how to streamline operations can be difficult within a complex system.
It’s been estimated that a staggering $4 trillion is spent per year on healthcare in the U.S. Roughly 25% of that is linked to administrative costs. Simplifying this aspect of care delivery could significantly reduce overall spending, according to McKinsey & Company. And healthcare data can provide clarity and help focus these efforts.
A healthcare analytics solution can help practices filter through massive amounts of information and gain insights into clinical processes. With this data, it’s possible to pinpoint inefficiencies and bottlenecks and develop solutions that address them.
This can enable clinical teams to more quickly adapt to or overcome challenges, creating more value for stakeholders and patients, alike. It can also help ensure the data collected by health information technology is viewed critically and accurately.
At ThoroughCare, we’ve helped more than 600 clinics and physician practices implement digital solutions to deliver treatment more effectively. Our software solution supports a patient-centered approach to care management, wellness, and transitions of care.
This article will provide a brief overview of healthcare analytics and review two ways this technology can help increase clinical capacity.
What is Healthcare Analytics?
Healthcare analytics involves the systematic interpretation of patient and clinical data. It is software that relies on underlying data science principles to help providers make intelligent decisions. Whereas EHRs, remote monitoring devices, wearables, and care coordination software will collect data, analytics can help make this information actionable.
There are many possible applications for this technology.
For example, analytics could be implemented to facilitate clinical research, mitigate health risks, or prevent cyber fraud. These are only three use cases of many. Because of its vast potential, significant investment has been directed toward these types of tools.
It is estimated that the healthcare analytics market segment will exceed $75 billion by 2026. Much of this growth can be attributed to federal mandates to implement healthcare IT, broader efforts to reduce spending, and initiatives that promote population health management.
There are five categories of healthcare analytics. These include:
Discovery: This is the aggregation of raw data to identify patterns or anomalies.
Descriptive: This is using data to understand historical trends or past performance. For example, this can help your practice track enrollment metrics for care management programs.
Diagnostic: This can help you understand why. Why have enrollment rates in a program increased or decreased? Diagnostic analytics is a way to dig further into the data to achieve an explanation.
Predictive: Using historical data, analytical models can forecast specific metrics or patient health risks with the help of machine learning. For example, what might a care management program’s enrollment rate be one year from now, based on specific variables that are constant?
Prescriptive: This method can help possibly alter the expected results implied by predictive analytics. If a program’s enrollment rate a year from now seems low, what can be done now (based on the data) to improve this outcome? However, this technology is currently limited in its capabilities.
The Value of Data Analytics for Clinical Operations
As noted above, there are many possible uses for data analytics in healthcare.
Below, we’ll cover two applications of this technology that can support clinical efficiency.
1. Workflow Optimization
Care coordination software or population health management platforms usually collect patient and provider data. As clinical staff uses such tools to coordinate services or manage patients, a digital footprint is left behind.
This raw data can include patient communications, such as phone calls or texts, types of services provided, and billing claims. Analytics can help filter this information to show segments of data. With these segments, it’s possible to apply descriptive and/or diagnostic analytics to derive actionable insights.
These data segments could include:
Successful patient phone calls vs. unsuccessful calls
How many patients with a specific condition receive X service/medication vs. how many patients with the same condition do not receive X service/medication
Claims that have been billed vs. claims that have been generated
By visualizing each of these data points and drawing comparisons or conclusions from them, it’s possible to identify gaps in administration needs or care delivery. Seeing these gaps can spur innovation and help eliminate costs, especially with regard to inefficiencies that were previously invisible.
This insight can aid the development of automation for administrative tasks or help streamline patient engagement and management.
Analytics, itself, will not solve these problems, but it can help focus your practice’s efforts on addressing them. Additionally, data makes it possible to test solutions and see the results of this action in real-time.
2. Mitigating Health Risks with Effective Care Management
Many practices utilize these services to help manage patient populations and work toward improved outcomes. Some pair these services with Remote Patient Monitoring (RPM) to incorporate the real-time collection of patient vitals through digital devices.
In this context, predictive analytics can help identify at-risk patients and support preventive medicine. The continuous assessment of vital signs can help providers spot potential health events before they occur.
A patient with hypertension is enrolled in CCM and RPM. They track and report vitals, such as blood pressure and pulse, daily. After some time, this patient begins to report abnormal vital signs, establishing a concerning new trend.
Their data lives within a patient management platform, like care coordination software. This tool includes analytics functionality. By assessing their data through this lens, it is determined that if the trend of abnormal vitals continues, the patient may require hospitalization due to a negative health occurrence.
Their provider uses this insight to engage the patient and call attention to specific concerns, offering interventions and guidance. The patient is then able to address their health needs prior to a potential emergency.
A primary goal of care management is to prevent or reduce hospital admissions.
Healthcare analytics can help provide actionable insight into your patient's health and your organization’s effectiveness. You can use this intelligence to improve administrative processes and provide more effective preventive care.