Gone are the days where buzzwords like ‘big data’ or ‘artificial intelligence’ only carried meaning in IT and analytics departments — leaders in nearly all healthcare departments make data-driven decisions. Whether you’re the Director of Marketing, Chief Operations Officer, or a nurse leader in charge of a team, it’s essential to have a strong understanding of how data can drive performance and provide continuous improvement.
The COVID-19 pandemic is a recent example of how healthcare data analytics is essential to the industry. Public health experts used data to identify which cities were being hit the hardest, hospital administrators analyzed data to optimize hospital resources, and telehealth leaders used data to identify groups that could greatly benefit from their services.
A powerful tool for successful decision-making
Understanding healthcare data analytics can be very powerful — you can learn how data is stored so you recognize what capabilities are feasible, you can create compelling arguments for funding a project, or you can identify at-risk populations that could benefit from various clinical programs. Healthcare data analytics skills are important to possess as you take on increasing responsibility and accountability for big decisions.
Trend #1: Financial analysis and revenue cycle management
Healthcare data analytics can be used to uncover what may be driving the less-than-optimal financial performance of a project, program, or service line. You can identify specific drivers of cost so they may be mitigated or eliminated. Analytics can even help to quantify the overall impact of specific issues or variables.
Revenue cycle management (RCM) is an area that is seeing considerable investment in analytics. Healthcare payments are unique in that the full payment is usually a combination of payments over several months from patients, insurance companies, and the government. The process can be complicated, and it is only exacerbated by the shift towards value-based care. Analytics are used to simplify the process.
Trend #2: Population health and care management
Analytics are used to identify populations that have the greatest amount of healthcare risks. This could be based on their medical history, demographics, or socioeconomic factors. Doing so allows healthcare providers and payers to create more personalized clinical programs for these groups, thereby making them more effective.
These programs can also be compared to healthcare industry benchmarks using data analytics. It’s always important to have a baseline to measure your performance so that you can identify areas for improvement, as well as areas to celebrate success!
Trend #3: Provider performance
Uncover trends in your provider healthcare data. Clinical data is essential for leaders to make decisions on what services need improvement or changes in utilization. Data in treatment effectiveness, success rates, peer comparison, and more can be used to improve the practice.
Furthermore, there is a growing spotlight on the appropriateness of care given by providers, and analytics are a key driver in this process. Data analysis can highlight clinical variations at the physician-level that result in waste, cost, misuse, and underuse.
Trend #4: Clinical operations
Dashboards are one analytics tool that clinical operations leaders find particularly valuable, particularly when analyzing big data, where data sets are too complex to be dealt with by traditional data-processing application software. These dashboards can be customized to the insights needed for a specific user or department. Dashboards are automatically updated with the most current information. You can likely imagine several scenarios in healthcare where swift and actionable insights would be helpful.
Predictive analytics and artificial intelligence
Predictive analytics uses statistical techniques to make predictions about future or otherwise unknown events. This allows healthcare leaders to identify possible issues before they occur, allowing them to mitigate or eliminate negative effects.
Respective to the trends listed above, predictive analytics can:
- Predict patterns in utilization and expected revenue
- Assess individual risks to prevent escalation from mild conditions to chronic disease
- Identify doctors that may soon fall outside of evidence-based parameters
- Utilize patient vitals information to predict possible deterioration of symptoms in the near future
Artificial intelligence (AI) uses algorithms that are designed to make decisions using real-time data. There are no pre-set responses or reports of past outcomes. AI allows healthcare leaders to make more precise and valuable interventions in real-time.
Data-driven decision-making
Creating an efficient and effective process that starts with the collection of big data in healthcare to the distribution of meaningful insights is a colossal task; one that payers and providers are going to spend considerable time on over the next decade. As the healthcare industry undergoes its digital revolution, and consumer data pours in, organizations will be expected to use this data to improve the healthcare delivery system.
Healthcare leaders are now expected to understand the pillars of healthcare analytics in order to make effective, data-driven decisions. Using data analytics in healthcare can improve patient outcomes, lower costs, enhance patient experience, and elevate business operations.
