The following is a guest article by Bal Heroor, CEO and Principal at Mactores
The healthcare industry generates enormous amounts of data daily, from electronic health records (EHRs) to medical claims, research studies, wearables, and other digital health devices. While abundant data can be beneficial, it also presents a significant challenge for healthcare organizations. These organizations must manage and store the data, protect it, and ensure that it is accurate and complete.
This article will explore the problems that healthcare organizations face when it comes to analytics and how these organizations can take a collaborative approach to solving these problems.
The Problems in Healthcare Data Management
One of the significant problems facing healthcare organizations is managing the vast amount of data they generate. With so much data coming in, healthcare organizations need help storing and analyzing it. Many healthcare organizations have implemented electronic health record (EHR) systems. But health records are strictly governed by privacy regulations, and are often siloed in different systems, both of which complicate the data integration process. In addition, EHR systems are not always interoperable, limiting data sharing between healthcare organizations.
Another significant challenge in healthcare data management is data quality. Healthcare data is often incomplete, inaccurate, or outdated, leading to errors in diagnosis and treatment. Ensuring that the data is high quality can be time-consuming and challenging.
One thing that can help here is data lakehouses, which combine the ease of use of data warehouses and with the relatively low cost of data lakes. Data lakehouses can consolidate data from multiple sources into a single system, providing a central location for data storage and management. Data lakehouses can also provide data quality checks and cleaning processes, ensuring data is complete, accurate, and up-to-date.
Data Security and Compliance
Data security and compliance are critical concerns in the healthcare industry. The sensitive nature of patient data means that it must be protected from unauthorized access or breaches. Healthcare organizations must comply with strict regulations, such as HIPAA, which mandate how patient data should be collected, stored, and used. Failing to comply with these regulations can result in severe consequences, including legal penalties, fines, and damage to an organization’s reputation.
Data lakehouses can help healthcare organizations manage their data security and compliance requirements by providing robust security measures like encryption and access control. Data lakehouses can also provide detailed audit trails that track all data access and usage, enabling organizations to demonstrate compliance with regulatory requirements.
Analytics and Insights
Data lakehouses can also help healthcare organizations derive insights from their data, leading to better patient care, more efficient operations, and improved outcomes. With so much data available, it can be challenging to analyze and make sense of it. Data lakehouses can provide a centralized location for data analysis, making integrating and analyzing data from multiple sources easier.
By combining data from various sources, healthcare organizations can gain a more comprehensive view of patient health, leading to better diagnosis, treatment, and outcomes. In addition, data lakehouses can support advanced analytics techniques, such as predictive analytics and machine learning, enabling healthcare organizations to identify patterns and trends in their data.
Workshop-Driven Approach
A workshop-driven approach can help healthcare companies solve analytics problems using data lakehouses by facilitating collaboration, alignment, and communication among key stakeholders. The approach involves a series of workshops that bring together individuals from various parts of the organization to identify business problems and opportunities that can be addressed with analytics.
The first step is to assemble a cross-functional team that includes representatives from IT, data governance, business owners and leaders, and an analytics team. This team will work together to define the workshop’s scope and identify the business problems and opportunities that need to be addressed. The team can also identify the data sources required to address these problems.
Next, the team will review the business problems and opportunities identified in the first step, then develop a set of analytics use cases that address these issues, define the specific data that needs to be analyzed, and decide on which analytics techniques should be used.
In the third step, the team will develop a data architecture and governance strategy to support the analytics use cases. This includes identifying the data sources and data transformations required. The team will also define the governance policies and procedures that will ensure the accuracy, completeness, and security of the data.
Next, the team will build the data lakehouse to support the analytics use cases. This includes ingesting, transforming, and storing the data. The team will also develop the analytics models and algorithms required to address the business problems and opportunities identified in the first step.
Finally, the team will deploy the analytics models and algorithms to address the business problems and opportunities identified in the first step. The team will also develop reports and dashboards that enable stakeholders to visualize the analytics results.
Benefits
The workshop approach helps healthcare companies address the analytics problems strategically and collaboratively. It also ensures that the data architecture and governance strategy are aligned with the use cases, making it easier to manage and analyze data in a way that ensures accuracy, completeness, and security.
Another benefit of a workshop-driven approach is that it encourages stakeholder communication and collaboration. Healthcare organizations are often siloed, with different departments working in isolation. By bringing together IT, data governance, business, and analytics representatives, the workshop approach promotes cross-functional collaboration and helps break down silos, leading to more effective problem-solving and better decision-making.
Also, the workshop approach is iterative, meaning the team can continuously refine and improve the analytics use cases as they gain more insights from the data. The approach is also flexible, meaning that the team can adapt to changes in the business environment and adjust the analytics use cases as needed.
Conclusion
Healthcare organizations face significant challenges when managing their data. However, a workshop-driven approach can help them overcome these challenges and solve analytics problems using data lakehouses. As the healthcare industry moves into a more data-driven and interoperable future, a collaborative approach will become increasingly valuable for healthcare organizations.
About Bal Heroor
Bal Heroor is CEO and Principal at Mactores and has led over 150 business transformations driven by analytics and cutting-edge technology. His team at Mactores are researching and building AI, AR/VR, and Quantum computing solutions for business to gain a competitive advantage.
