Data is a fundamental part of the healthcare industry, and yet, studies show that companies barely utilize 10-15% of the total available data. However, clinical data analytics is now emerging as an efficient tool that can help clinicians improve patient care experiences, procedures, and therapies.
With the help of clinical data analytics solutions, healthcare providers can record and evaluate patient health information to understand current trends and predict future trends. The significant strategic and operational insights provided by transforming unstructured data into meaningful and valuable information facilitate crucial healthcare processes and better clinical decision making.
The common bottlenecks which might slow down your business growth
The sheer volume of patient data that exists through electronic health records is untapped for its clinical predictive analytics potential.
Failing to keep up with the constantly shifting payment models is taking a toll on overall revenue and operating systems.
Failure to identify at-risk patients and manage healthcare on the basis of risk factors of select populations.
The traditional system of healthcare management is falling short to address the growing needs of a value-based care system.
An increasing number of patients in the healthcare system require measures that are of a higher quality for accurate care giving.
The adoption of technologies towards clinical analytics software is providing a competitive edge to organizations, leaving traditional models far behind.
Reshape your organization with advanced analytics for improved customer experiences
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.
OSP has worked with Stephen to create a mobile health application offering 'Doctor on Demand'. This mhealth solution is based on the Uber model to enhance the availability of health access in the US.
The outcome performance of a leading hospital was measured, wherein the effect of the care provided on patient health was monitored to determine mortality rates, range of blood pressure and other vitals, patient abilities toward healthy living, etc. Furthermore, this form of analytics offered insights, through clinical predictive analytics, on the hospital environment toward acquiring infections, susceptibility to accidents, clinical decision support, etc.
We deployed an analytics approach toward a hospital that was struggling with increased rates of infections post-implant surgeries. Through clinical benchmarking analytics, it was realized that the particular brand and model of implantable device being used created an added susceptibility to acquiring infections. Replacing that brand mitigated the causing of infections post-implant surgery.
Leveraging clinical decision support for the nursing staff at a clinic ensured continual compliance toward operational and nursing protocols through the use of tracking and reporting measures being displayed, along with routine updates on the critical factors of each patient. This was done through an ongoing automated analysis of each patient’s health condition, with alerts and reminders for care.
To gain information as to the likelihood of a patient in developing complications or general monitoring of the vital health condition of patients after leaving a hospital, wearable devices are used that undertake clinical research analytics to estimate likely readmissions. It further allows physicians to monitor the patients and intervene when necessary.
Our clinical research analytics data models are geared to rapidly adapt to the specific requirement of our customers with significant flexibility for continual adjustments. At OSP Labs, as with few clinical data analytics companies, we adopt bus architecture for holistic analytics solutions. Our models undertake speedy clinical research analytics with fast loading time and adapt to new requirements and data sources seamlessly.
OSP Labs is among the limited clinical data analytics companies that offer a simple, yet dynamic metadata repository that can efficiently undertake predictive analytics clinical trials and offer widespread use of meaningful data in an organization. Our data warehouse can operate efficiently to provide subjective relevancy and offer intelligent analysis, along with a routine reference to source origin.
Our data sets undertake predictive analytics clinical trials and clinical laboratory analytics through the use of new sources of data that can easily explore unique patient and provider identities from cross-border origins. Our data binding ability is easily adaptable to changing policies and vocabulary that can constantly provide the organization with the information relevant to their subjective need.
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