Clinical Data Analytics

Clinical data analytics analyzes clinical information generated from operations to harness useful insights into processes like patient care and other medical workflows. The insights derived from implementing a clinical data solution are used to improve the quality of care, reduce healthcare costs, improve population health, increase revenue, and optimize operations at medical organizations. Many industries use various kinds of data analytics to improve the speed and efficiency of operations and maximize revenues. In healthcare, clinical data analysis benefits all the stakeholders involved.   

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Robust clinical data analytics software solutions with built-in performance measurement systems allow clinicians to analyze and summarize patient information collected over time in the EHR systems.

Clinical data analytics can easily collect data related to various aspects of health system performance, such as population health, patient outcomes from treatment, clinical quality, CDSS, and the quality of care, responsiveness, and productivity. An analytical approach helps healthcare providers in making critical decisions on population health, organization performance, and improving patient satisfaction. Performance measurement systems also help clinical practitioners review their performance in comparison to their industry peers.

Research states that misdiagnosis and unnecessary or inaccurate patient care results in an annual loss of roughly $17-29 billion. Clinical Decision Support Systems can help to lower this cost and improve overall efficiency.

Clinical Decision Support Systems (CDSS) use clinical data analytics to determine relevant correlations and make predictions to improve patient care. Healthcare organizations can administer timely treatment, reduce medication errors, enhance patient satisfaction, plan future care strategies, and reduce operational costs.

CDSS can help clinicians with data entry, data review, assessment, and understanding of patient health information and sending automatic alerts to patients triggered by specific events.

With the constantly changing, complex federal reporting conditions, healthcare organizations are looking for regulatory reporting tools that provide a wide range of functionality for quality improvement.


Clinical data analytics tools with regulatory reporting features help clinicians gain comprehensive insights into patient readmissions, treatment costs, clinical decision making, and organizational efficiency.


Intuitive clinical data analytics solutions provide clinical-abstraction guidelines that help providers filter data according to legal mandate specifications. Data visualization through advanced dashboards with customized filters allows providers to view pertinent patient health data. 


Other benefits of regulatory reporting analytics include instant alerts, efficient data-upload processes, single sign-on functionality for easier access to EMR data, and detailed reports with patient-compliance data.

High-quality data is a fundamental requisite in healthcare to derive accurate conclusions. Healthcare organizations that lack a robust data framework rely on manual, ad hoc processes to evaluate data quality. The manual quality management process involves periodically reviewing data to identify and remove outliers and data issues.

Clinical data analytics focuses on improving the overall quality of caregiving. Quality Assessment analytics solutions first match source data against pre-defined data quality rules. The smart system then identifies problematical areas concerning patient health, higher organizational costs in specific areas, bottlenecks and display results in the form of insightful and actionable reports.

Healthcare professionals waste a lot of time and effort needlessly repeating tasks in identifying and implementing best practices.

Clinical Benchmarking is a useful quality management tool that helps in calculating and analyzing an organization’s caregiving strategies and outcomes in comparison to its peers to diagnose the best approach.

Benchmarking also helps in identifying areas of improvement and the causative factors in different areas of healthcare so the management can make the required corrections in current practices. Organizations can thus gain credibility as patients feel assured about the efficacy of existing care services.

Health informatics, also called Health Information Systems, uses information technology systems to collect, analyze, and manage health records to improve patient outcomes.


Health Informatics employs informatics concepts, theories, and practices in real-life circumstances to improve the quality and safety of patient care. Health informatics helps healthcare providers implement new clinical decision support systems, update existing information, and enhance interoperability between healthcare systems. 

Clinical data analytics as a service foster better interaction among various healthcare providers such as hospital staff management, insurers, and health information specialists, for easy access to a particular patient’s records electronically.


This sector of analysis is particularly important because it is used to drive medical decision making. The data provided through analysis is used to indicate gaps in care, problems with the provision, and opportunities for cost savings and management.


Hospitals generate large amounts of data from day-to-day operations. This includes patient care, diagnoses, treatments, and administrative activities. OSP can design clinical data software to enable administrators and clinicians to identify inefficiencies in the system and know how they can be addressed. As a result, managers can optimize workflows, leading to greater efficiency and, finally, facilitating better care for the patients.

This is one of the most promising features of clinical software solutions. By analyzing patients' historical and current health data, doctors can identify who might be more likely to develop certain diseases. This insight enables doctors to prescribe preventive actions for the patients before the disease becomes full-blown. Unlike the usual reactive approach, this proactive approach to care is considered among the best ways to handle chronic diseases.

Hospitals carry out a large number of activities each day when it comes to providing care. These include conducting tests, and scans, treating patients, carrying out billing operations, handling human resources, etc. But we can build clinical data analytics platforms to assess all the information from these activities and identify ways to optimize them. These insights help administrators to take measures to improve speed and productivity while lowering costs.

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