When you are looking to improve the operation of your home health agency, you have a number of tools at your disposal. Perhaps the most powerful tool, though, is data. Whether you use data collected on a national scale to determine best practices for your agency, or leverage the data that you collect about your specific patients, the information that’s being collected and analyzed allows you to more effectively identify patterns that influence care delivery, better predict patient behavior and outcomes, and identify possible issues that could lead to adverse events. Taken together, these improvements help reduce healthcare costs and improve outcomes, while significantly improving patient quality of life.
Therefore, it’s becoming increasingly important for home health agencies to invest resources in the technology and training necessary to effectively put data to work in improving the day-to-day operations of the agency. Thanks to more powerful home health software, you most likely have significantly more data than ever before. Putting this data to use allows you to better customize patient plans that reduce the likelihood of readmissions, and allow you to better meet your goals for patient outcomes and satisfaction.
ACOs, Reduced Readmissions, and Cost Savings
One of the major changes brought about by the Affordable Care Act was the idea of Accountable Care Organizations (ACO). The primary goal of an ACO is to provide more coordinated care for patients, ensuring that they not only get the care that they need when they need it, but that the costs are kept as reasonable as possible. These voluntary organizations not only reduce costs, but they also help prevent the unnecessary duplication of care and reduce medical errors. When the organization meets savings goals, the members collectively share in the savings in the form of increased Medicare reimbursements.
As home health agencies become more integral to the success of ACOs, CMS is also expanding cost-saving incentives beyond hospital readmission rates, and focusing on specific procedures. In other words, it’s not simply whether an HHA can keep patients out of the hospital after discharge, but on how well they actually provide care to those patients overall. When an agency has actual hard data to show that they can provide the best care for their patients, they are more likely to be secure referral partners, and become an even bigger part of the ACO model.
This means that it’s more important than ever for your agency to learn how to better use the data you collect to make changes and improvements to care. A growing number of agencies are turning to home health agency management software that allows for more streamlines assessments and patient visits, while also improving documentation. These programs allow agencies to easily compare their performance against local and national averages, as well as CMS benchmarks, to identify areas in need of improvement. While reducing readmissions is a primary goal and driver of home health partnerships — especially since CMS has been penalizing hospitals with excessive readmission rates and/or unsafe discharges for the last five years — CMS is also looking to reduce the overall number of home health visits per patient. By analyzing agency data, it’s possible to identify the trends and practices that lead to efficiencies, and provide quantifiable evidence to support care plans.
Data Analysis vs. Data Mining
Getting the most from your agency’s data requires more than simply looking at the numbers and comparing them to benchmarks. Deeper analysis is necessary, and that means becoming familiar with data mining.
Data mining is different from data analysis. Data analysis, particularly predictive analysis, looks at measurable variables and predicts an outcome. For example, if a patient enters home health with a particular condition with co-morbidities, by comparing that patient’s data against other patients with similar conditions, you can predict the potential outcomes and design a care plan accordingly. However, truly using your data to its greatest advantage requires going beyond looking at existing data and trends, and finding the relationships between different measurable variables.
One reason that data mining tends to be more effective in the home health environment is that assessing patients’ outcomes across home healthcare providers is often challenging, thanks to the nearly endless list of possible risk factors and outcomes for home health patients. Further complicating matters is that these factors are so variable, and have different levels of interdependence. The sheer number of variables in the delivery of home health has led some to suggest that traditional statistical methods for interpreting data aren’t effective for analyzing home health.
Data mining, though, is proving to be an effective means of driving better outcomes. By looking for connections and meaning among data patterns, home health agencies have been able to improve care; in fact, in two separate instances, data mining has improved patient safety and reduced medication errors.
Again, effective data mining requires an investment in the tools and techniques necessary to uncover the patterns and find the connections within them. Doing so will pay off though, in a more efficient agency and better outcomes for patients.
To learn more about software that can help with this effort, click here to see more about Complia Health’s family of agency management software and tools.