Data Analytics and Reporting
Expert-defined terms from the Certified Professional in Electronic Health Records Documentation and Coding course at London School of Business and Administration. Free to read, free to share, paired with a globally recognised certification pathway.
Analytics #
The systematic computational analysis of data or statistics. In the context of data analytics and reporting for electronic health records (EHRs), analytics involves extracting insights and knowledge from EHR data to improve patient care and outcomes. This can include predictive analytics, which uses statistical algorithms and machine learning techniques to identify patterns and trends in EHR data and make predictions about future events, such as the likelihood of a patient developing a particular disease.
Big Data #
Large, complex datasets that cannot be easily managed or analyzed using traditional data processing techniques. In healthcare, big data may include EHR data, clinical trial data, genomic data, and other types of health-related data. Analyzing big data can help healthcare organizations identify patterns and trends that can inform decision-making and improve patient care.
Business Intelligence (BI) #
The use of technology and data analytics to support business decision-making. In healthcare, BI tools can be used to analyze EHR data and other types of health-related data to support a variety of functions, including population health management, quality improvement, and cost containment.
Clinical Decision Support (CDS) #
A type of technology that provides healthcare providers with evidence-based recommendations to support clinical decision-making. CDS systems can be integrated into EHRs and can provide alerts, reminders, and other types of support to help providers make informed decisions about patient care.
Clinical Documentation Improvement (CDI) #
The process of improving the quality and accuracy of clinical documentation in EHRs. CDI programs often involve the use of technology and data analytics to identify documentation gaps and opportunities for improvement.
Data Dashboard #
A visual representation of key performance indicators (KPIs) and other data points. Data dashboards can be used to monitor and track progress towards specific goals and can help healthcare organizations make data-driven decisions.
Data Governance #
The processes and policies that an organization follows to ensure that its data is accurate, complete, and secure. In healthcare, data governance is essential for protecting patient privacy and ensuring that EHR data is used effectively to support patient care and outcomes.
Data Mining #
The process of discovering patterns and trends in large datasets. In healthcare, data mining can be used to analyze EHR data and other types of health-related data to support a variety of functions, including population health management, quality improvement, and cost containment.
Data Reporting #
The process of generating and distributing reports that summarize and analyze data. In healthcare, data reporting is an important tool for monitoring and tracking progress towards specific goals and for making data-driven decisions.
Data Visualization #
The process of representing data in a graphical or pictorial format. Data visualization can be used to help healthcare organizations understand and communicate complex data in a clear and concise way.
Data Warehouse #
A large, centralized repository of data that is used for reporting and analysis. In healthcare, data warehouses can be used to store EHR data, claims data, and other types of health-related data for analysis and reporting.
Electronic Health Records (EHRs) #
Digital versions of a patient's medical record. EHRs can include a variety of data, including demographic information, medical history, medication lists, and laboratory results.
Health Information Exchange (HIE) #
The electronic sharing of health information between organizations. HIEs allow healthcare providers to access and exchange patient data in real-time, improving coordination of care and patient outcomes.
Population Health Management (PHM) #
The use of data analytics and other tools to improve the health of a defined population. In healthcare, PHM programs often involve the analysis of EHR data and other types of health-related data to identify trends and patterns that can inform decision-making and improve patient care.
Predictive Analytics #
The use of statistical algorithms and machine learning techniques to identify patterns and trends in data and make predictions about future events. In healthcare, predictive analytics can be used to identify patients at risk for developing certain diseases, predict patient readmissions, and support other functions.
Quality Measures #
Metrics that are used to assess the quality of healthcare services. Quality measures can be based on a variety of data, including EHR data, claims data, and patient-reported outcomes.
Reporting Tools #
Software applications that are used to generate and distribute reports. In healthcare, reporting tools can be used to analyze EHR data and other types of health-related data to support a variety of functions, including population health management, quality improvement, and cost containment.
Structured Data #
Data that is organized in a specific format that allows it to be easily searched and analyzed. In healthcare, structured data may include demographic information, medication lists, and laboratory results.
Unstructured Data #
Data that does not have a specific format and is not easily searchable or analyzable. In healthcare, unstructured data may include clinical notes, radiology reports, and other types of free-text data.
Visual Analytics #
The use of data visualization and other techniques to support data analysis and decision-making. In healthcare, visual analytics can be used to help healthcare organizations understand and communicate complex data in a clear and concise way.
Workflow Analytics #
The analysis of healthcare workflows to identify inefficiencies and opportunities for improvement. In healthcare, workflow analytics can be used to optimize clinical processes and improve patient care.