Using Data-Driven Decision-Making for Laboratory Success

A marked change in approach is needed in healthcare, especially in clinical decision-making. Poor clinical decision-making has plagued the sector, leading to poor patient data collection, poor intellectual ability to update information, and poor patient data processing (2022). Fortunately, recent estimates by healthcare professionals say that using data-driven technologies is a promising opportunity for improving safety and quality of patient care (2021).

In this blog, we want to harness the power of data-driven decisions in healthcare and share how you can implement best practices for your lab’s success.

What is data-driven decision-making?

According to Harvard’s Business Insights (2019), data-driven decision-making or DDDM is the “process of using data to inform your decision-making process and validate a course of action before committing to it.”

Rather than relying on intuition or experience alone, DDDM allows you to make choices based on data analysis. As a result, decision-makers use advanced analytics tools, such as predictive modeling and machine learning algorithms, to be accurate and effective, refining their approach based on new data and insights.

Why applying data-driven quality improvement in healthcare matters

As healthcare is an information-intensive industry, data generated daily through patient records, diagnostic tests, treatment plans, and more pile into a vast wealth of information. Annually, the healthcare sector produces at least 19 terabytes of clinical data. These numbers are growing exponentially, and providers are turning to data-driven quality improvement to gain several benefits, such as:

  • Improved patient outcomes: By analyzing patient data, healthcare providers can find trends in patient information to help detect possible disorders early or those at risk of readmission. Predictive analytics allow proactive measures, ultimately improving patient outcomes.
  • Reduced healthcare expenses: Finding areas where healthcare costs can be reduced is a hallmark feature of DDDM. It ensures that wasteful spending patterns, process inefficiencies, and underutilized resources are identified and adjusted for maximum effectiveness. This frees up funds to reinvest in patient care and innovative technologies.
  • Data-empowered strategic planning: Healthcare providers can expand their services by developing long-term strategies with data-driven decision-making. By analyzing patient demographics and market trends, healthcare companies can meet the evolving needs of the population.

By 2024-2030, the healthcare analytics industry is projected to grow from approximately $43.1 billion in 2023 to a compound annual growth rate (CAGR) of 21.1%. There is no wonder why – data-driven decision-making empowers healthcare providers with the insights they need to succeed and better care for their patients.

Implementing data-driven strategies for your lab’s success

Becoming a data-driven laboratory is essential for leveraging huge volumes of data and achieving revenue-generating opportunities. To begin the process, you should first identify data-related goals and objects, evaluate the amount of data your lab uses, and analyze your current data structure and analytics tools.

Below, you’ll find the key components that help data-driven decision-making in healthcare, ensuring your road to success.

  • Electronic Health Records (EHRs) – These centralize patient information and help give a holistic view of a patient’s medical history, medications, and treatment plans. Laboaraties can invest in interface platforms and development resources to enable LIS integration with EHR systems. At Synapse Lab Billing, we leverage connectivity to the practice’s EHR, helping laboratories streamline decision-making from the lab to the clinicians.
  • Real-Time Monitoring and IoT Devices – These devices have transformed conventional laboratory management by deploying invaluable data in real-time that defines crucial integration areas, choosing appropriate equipment, and guaranteeing connectivity and security procedures.
  • Advanced Analytics and Predictive Modeling – Uncovering patterns to forecast future trends based on historical data is critical for data labs. This ensures that high-risk patients are identified, resource allocation is optimized, and decision-making is data-informed. That’s why experts at Synapse Lab Billing integrate efficient systems that produce meaningful data that forges a path to your success.

Develop a data-driven approach to healthcare with Synapse

Data-driven decision-making is a transformative force in healthcare, and we at Synapse Lab Billing offer the pathway to harness its ability to personalize and create efficient and effective patient care. Our integration of advanced analytics, real-time monitoring, and comprehensive EHR systems can quickly and significantly impact your healthcare practice’s patient outcomes and efficiency. We employ highly competent and trained teams to manage your lab’s analytics.

Embrace the full potential of healthcare’s success with data analytics. Shape the next era of your lab delivery with Synapse. You may contact us at (844) 384-7532 or medicalsales@synhs.com

Sources:

Abate, H. K., Birhanu, Y., & Gebrie, M. H. (2022). Clinical decision making approaches and associated factors among nurses working in a tertiary teaching hospital. International Journal of Africa Nursing Sciences, 17, 100432.
https://doi.org/10.1016/j.ijans.2022.100432

Cascini, F., Santaroni, F., Lanzetti, R., Failla, G., Gentili, A., & Ricciardi, W. (2021). Developing a Data-Driven Approach in Order to Improve the Safety and Quality of Patient Care. Frontiers in public health, 9, 667819.
https://doi.org/10.3389/fpubh.2021.667819 ,/p>

Healthcare Analytics Market Size, share & Trends analysis Report by type (Descriptive analysis, predictive analysis), by component (Software, Hardware), by delivery mode, by application, by end-use, by region, and segment Forecasts, 2024 – 2030. (n.d.).
https://www.grandviewresearch.com/industry-analysis/healthcare-analytics-market#:~:text=Healthcare%20Analytics%20Market%20Size%20%26%20Trends,21.1%25%20from%202024%20to%202030

Krishnan, Sundar & Shashidhar, Narasimha. (2019). eDiscovery Challenges in Healthcare. 30-43.

Stobierski, T. (2019). The Advatanges of Data-Driven Decision-Making. From:
https://online.hbs.edu/blog/post/data-driven-decision-making