The recent wave of digitization in healthcare has made it more difficult for hospitals and health systems to manage the data they produce. New patient information is collected daily into various, disparate sources such as electronic medical records (EMRs) and other internal databases in different departments or facilities.
This makes coordinating care between healthcare providers (HCPs) challenging, as there is no unified data source; instead, data are fragmented within and across systems.
For evidence-based decisions, data must be accurate and robust. A 2017 study found that, despite the benefits of consistent, well-documented, and trustworthy data, 56% of hospitals lack strategies for managing data availability, integrity, and security (data governance). This slow uptake of big data tools could be attributed to factors such as:
However, systems enabling data governance could help HCOs realize the possibilities and promise of health analytics and overcome the challenges of the explosive growth in healthcare data. Therefore, it is important for HCOs to consider how to modernize their health data management systems.
Health data management is not new — there have always been patient health records to maintain and compile, whether they are the traditional handwritten notes or electronic medical records (EMRs). However, modern health data management systems need to collect, store, and review large volumes of patient data in a single, secure, compliant, scalable system. The incorporation of analytics makes it possible to report and analyze those data for quality improvement and research initiatives.
Even for HCOs that have implemented a digital health data management system, extraction and analysis of data for use initiatives are traditionally conducted manually, with the need to review each record separately. Not surprisingly, it can take considerable amounts of time and human resources to reach any conclusions.
Artificial intelligence (AI), particularly machine learning (ML), is revolutionizing the way we handle large amounts of data in healthcare, providing HCPs with integrated information—across health systems and worldwide—to improve the quality of patient care. These improvements are manifested through higher quality patient data that can be used to make treatment decisions for similar patients as well as to implement precision medicine in which the most successful treatment choice can be predicted based on patient attributes, history, and treatment context.
Using algorithms that resemble human thought processes, a patient’s history and interactions throughout their care journey can be quickly accessed and analyzed, which can be of particular importance during an emergency situation. And, thanks to AI and ML, computers are able to continuously learn and improve their analytics capabilities, for better predictions and treatment choices for future patients.
In addition, AI and ML can help with the critical task of validating and standardizing data for greater usability across different systems and initiatives. Software solutions that are able to make sense of both structured (e.g., ICD codes) and unstructured (e.g., clinical notes, lab findings, imaging) data for these purposes are especially useful.
Health data management systems powered by high-end AI and ML software solutions are capable of handling big data, providing HCPs the tools they need to improve their patients’ health, and public health in general. With the reduction in manual chart review, HCPs have fewer tasks competing for their time and resources, and they gain access to higher-quality data.
Because Carta Healthcare recognizes the value of big data produced by the healthcare industry for patient care, we developed a suite of AI-enabled solutions to automate data collection, abstraction, and more from healthcare systems. Our suite of smart solutions offer nearly endless opportunities to leverage technology for precise, efficient, and impactful interventions for both patient care and hospital operations (e.g., supply chain management, staff scheduling, optimization of operating room utilization).
The result is data and technology that hospital administrators, clinicians, and abstractors can trust to incorporate in their care mechanisms and scale as data volumes and need for analytics continue to grow.
Begin your journey from data to actionable insights with Cartographer today.