M&A growth strategies must include master data management initiatives so health systems can break free of their reporting backlogs and meet the rising demands of advanced data analytics. Making incremental improvements to tackle waste and improve processes starts with strategies for self-service analytics.
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This article is based on a healthcare conference presentation by Yoshi Williams, MBA, Data Warehouse Manager & Principal Architect, MultiCare Health Systems titled, “How to Break Free of the Reporting Backlog and Meet Rising Demands for Data and Analytics.”
After a dip in 2021, mergers and acquisitions are again in full swing. These business deals result in highly interconnected networks of hospitals, behavioral health facilities, rehabilitation centers, and specialty clinics, among other providers, giving patients opportunities to engage in more comprehensive medical services. An uptick in M&A activity also allows health systems to leverage advanced data analytics.
Indeed, with greater accessibility, this intricate web of clinicians, medical records systems, and healthcare data comes with the challenge of effectively collecting, protecting and sharing information. When health systems rely on obsolete operating systems that fail to meet today’s healthcare data analytics needs, resulting in delayed reporting and a backlog of reporting requests, everyone loses.
Therefore, the consolidation of healthcare providers and health systems requires a modern technology platform, one that supports healthcare analytics, workflow applications, and data portability with minimal outlay and that manages deep data across disparate sources.
With the right healthcare analytics tools, providers gain critical knowledge to engage patients, intervene early, and prevent unnecessary and costly treatments. Additionally, analytics teams, including quality improvement leadership, researchers, and care management coordinators, can glean meaningful insights that enable evidence-based decision-making concerning clinical care, resource allocation, and cost-saving measures.
By leveraging new technologies, all players in the healthcare industry can position themselves for successful mergers and acquisitions that drive growth and support their bottom line.
A self-service healthcare analytics tool in healthcare settings enables analyst teams to uncover data and information bottlenecks, duplicative and inaccessible solutions, inadequate data governance, and data quality flaws. Such tools also allow for the standardization and reuse of analytics assets, liberating data applications from the control of SQL experts. Additionally, these applications provide business and clinical users with a self-service option, reducing analyst teams’ workload.
This article explores the challenges to eliminating reporting backlogs and the steps to adopting a self-service healthcare analytics application that enables users to independently access, analyze, and visualize data without relying on specialized technical skills.
A dedicated analytics team is responsible for creating and supporting hundreds, if not thousands, of reports that are essential for delivering patient care. These reports provide valuable insights, such as quality metrics, clinical outcomes, financial performance, and operational efficiency. They enable departments, practitioners, and leaders to make informed decisions based on data-rich insights rather than intuition or guesswork.
Yet, the analytics team’s job has its challenges. From managing large volumes of complex data to ensuring data accuracy and security, there are several obstacles these teams must overcome to deliver meaningful analysis, including:
That said, all these challenges are avoidable. As it concerns master data management, collaborating with an experienced technology partner can be a game-changer for healthcare systems undergoing consolidation or procurement.
When considering new advanced data analytics software adoption, healthcare IT specialists play a critical role in identifying and reducing organizational waste. Waste often involves inefficiencies or repetitive or unnecessary steps that perpetuate data siloes and hinder the effective use of an organization’s talent and technology.
To that end, an organization might rely on a process improvement method originating from the Lean methodology, such as The Eight Wastes, which centers efforts on specific areas that drain organizational resources.
The following outlines the areas, along with possible solutions, using emergency departments as an example:
Undoubtedly, self-service healthcare analytics empowers health systems to unlock the value of their data, democratize access to insights, and inform decision-making, which ultimately promotes a data-driven culture, operational efficiency, and improved patient outcomes.
Functionality is the first factor to consider when selecting an advanced data analytics platform. Look for a solution with robust capabilities such as predictive modeling, machine learning algorithms, and visualization tools. The platform should also have enough flexibility to customize these features based on a health system’s unique requirements.
Scalability is equally important when evaluating an advanced data analytics solution. As the organization grows and generates more data, the platform must handle increasing volumes of information while maintaining optimal performance levels. The selected solution should provide scalability without sacrificing efficiency or compromising analytical accuracy.
Involve key stakeholders, such as clinicians, administrators, and analysts, in the migration process. Understand their specific needs and requirements for data analysis and collaborate with them to design the new healthcare analytics platform.
As these advanced data analytics applications become more critical to an organization's operations, it's important to continuously monitor their performance and usage to ensure they meet clinical, operational, and financial needs.
To that end, collecting feedback from users and stakeholders plays a crucial role in identifying areas for improvement and optimization. Through regular surveys, focus groups, or one-on-one interviews, organizations can gather valuable insights that can help them improve platform functionality, usability, or performance. By considering user feedback, hospitals and health systems can prioritize advanced data analytics enhancements or bug fixes that could significantly impact overall platform effectiveness.
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