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With so much data being produced and collected, it’s easy to become perplexed when considering efficient and profitable ways to process the information.
The biggest mistake experienced by many healthcare organizations is thinking that the status quo is sufficient when it comes to data integrity and the analysis of data. The common workaround is high cost manual processes and procedures to supplement gaps in an existing process, such as financial reporting, claims processing, and regulatory mandates, to ensure that data across the enterprise is accurate. It often becomes evident that process automation is the method to reduce costs, improve productivity, and reduce risks during new systems refreshes, changes in regulations, and mergers and acquisitions.
Here are a few tips to change the status quo, avoid high cost manual processes and implement a process automation that provides the information your organization needs.
- Start by asking questions: What does your organization want to learn from the data? What is a desired outcome? By understanding what your organization wants to achieve, it makes it easier to focus on what data should be gathered in addition to how to process, manage and share the information. To create the best questions, strongly define the desired outcomes and create performance and progress measurements to use the data optimally.
- Have one data analysis platform: In order to perform advanced data analysis, organizations need a standardized and scalable end-to-end data analysis platform that can replace siloed processes and improve operational efficiencies. The siloed data approach is difficult to audit and the silos make it difficult to find patterns and actionable insights across disparate sources of information. The concept is to non-intrusively analyze data across your existing internal and external systems and embed the results into streamlined workflows that change the way you do business.
- Protect member privacy and facilitate fraud detection: A robust technology platform can help with data security for patients and avoid any Health Insurance Portability and Accountability Act (HIPAA) violations. Further, advanced analytical systems can monitor fraud – whether to identity fraudulent activity or to check the accuracy and consistency of claims.
- Use predictive modeling: Cost savings are a key focus for healthcare organizations. Using advanced data analysis like predictive modeling with information such as member profiles can proactively help identify people who may benefit from preventative care or changes to their lifestyle. Predictive modeling can also be used to pinpoint predictive events and create prevention initiatives. This data can help motivate members to take a greater role in their own healthcare by providing insights that encourage them to take preventative action to improve their health based on factual information. With the Affordable Care Act’s new payment models, healthcare organizations must improve quality ratings and reduce re-admissions, all areas that predictive modeling can help.
- Act on the outcomes: An end-to-end platform must not stop at the analysis phase for hand-off to other systems, but rather be able to embed the analysis results into your existing processes to fully realize the value in terms of bolstering profitability and improving the member experience. It is a challenge to act on the insights from analysis, but to make the most out of the analysis, pick one small, actionable plan that would affect the majority of employees and/or members. With data analysis improving the data integrity, accurate insights can also be useful for senior executives who need to make strategic decisions about the organization’s future direction.
The staggering amount of healthcare data available can be overwhelming to healthcare organizations and automation across various systems to streamline processes are crucial to realize both efficiency gains and prompt actionable results.