The average hospital generates 50 petabytes of data every year – more than twice as much information as is housed in the Library of Congress. But in order to manage and make that data useful, it must be reported, stored, and analyzed accurately – which is why clinical data analysis and reporting systems are so important.
Clinic data reporting is a vital part of that process–in which practitioners, analysts, and medical professionals collect, structure, appraise, and report information extracted from data, often from multiple sources. This article explores how your practice can improve its reporting and analysis processes.
Clinical data reporting is a data management process that involves the collection, organization, analysis, presentation, and dissemination of clinical data gathered from various sources. The goal is to store accurate information about patient health, enable providers to gain a clear overview of the patient’s needs, and enhance workflow productivity in healthcare operations.
As a few examples, a healthcare reporting project or analysis initiative might require data from sources across clinical, financial, and administrative systems, such as:
This data can be analyzed to determine outcomes, patterns, correlations, patient risk factors, and treatment strategies for use in clinical trials, patient and disease registries, or for accurate administration purposes.
Clinical data reporting is more than just data gathering. With clinical data reporting, along with proper diagnosis coding, providers can render better services and eliminate inefficiencies in their workflows. Here are some reasons why you shouldn’t overlook clinical data reporting:
Clinical data reporting can help you employ predictive analysis and improve the overall delivery of care. With the right practices and tools, you can collect and use past and current data to anticipate potential medical risks and proactively prevent them, making better use of your resources and preventing patients from dealing with severe outcomes.
The answer to most diagnostic queries lies in patient information. Clinical data reporting gives you access to data that you can use to address gaps in performance and optimize patient care. You can also use the information to make important decisions about healthcare operations.
Clinical data reporting doesn’t just deal with patient information. It also touches on facility and clinician data, which can be used to provide feedback to staff and improve workflows and performance. Having efficient staff is a great way to enhance patient care and keep people motivated to deliver the highest standards of care.
Clinical data reporting can give you an insight into operation metrics that correlate to your daily expenses. You can use it to keep track of your finances and streamline your budgeting. This way, you can reduce operational costs without compromising operational efficiency.
The complexity of data extraction and reporting and the number of potential data sources can make it challenging for healthcare services and providers to remove any potential for error while ensuring they have collected every piece of relevant data concerning each patient, claims history, treatment plan, or service type. In most cases, the final aim is to conduct data collection without any manual interventions, using integrations with EHRs and electronic medical records (EMRs) alongside other potential data flows.
Conventionally, tolerance rates applied to manually collated data were based on 0.5% across the healthcare industry, with data sampling used in studies and clinical trials based on standardized quality controls. Where data is captured, logged, imported, and organized electronically, the process becomes automated without needing acceptable error thresholds.
The higher the quality of the data extraction process used, the faster the data can be cleaned and structured consistently, and the better the quality of the information gleaned from that data will be. This quality improvement leads to greater productivity, accuracy, timely record-keeping, and reporting compliance – all of which are benefits of the right analysis and report system.
Reporting systems pull together all the data that matches project objectives into one place. Clinical data reporting might be used to gather data related to one patient, condition, practice, or treatment protocol.
Ultimately, data reporting systems ensure that users do not skip one potential input source or bypass a set of data that would have a material impact, often due to data being labeled or categorized differently or being stored in an inconsistent format.
Depending on the use of the data being collated through a clinical data reporting system, healthcare organizations will need to comply with professional standards and regulations. Rules are in place to reinforce good practice, the accurate measurement of outcomes, and ensure data is collected in a timely manner.
Clinical data reporting is commonly used in research projects or performance appraisals, where reusable data is leveraged to support faster, data-backed decision-making. In the pharmaceutical industry, data collection might need to adhere to standards set by the Clinical Data Interchange Standards Consortium (CDISC). This non-profit international organization produces models to help researchers and medical teams gather, manage, and report clinical data.
Data reporting requirements for purposes specific to a practice, healthcare service, or clinical provider may vary but will always need to follow guidelines around patient confidentiality.
There is a large market for data extraction and collection tools that integrate with your EHRs, healthcare data exchanges, and other key systems. This makes it far easier to collect clinical data, saving providers hours of manual effort and ultimately freeing them to focus on patient care.
The sheer volume of clinical data stored within most clinical data systems makes analysis a major challenge. Artificial intelligence (AI) tools can accelerate and improve analysis by leveraging sophisticated algorithms to produce relevant outputs, such as HCC coding, from even complex and unstructured data sets.
HCC Assistant leverages natural language processing (NLP) to ingest, document, and analyze both structured and unstructured clinical data from a wide range of sources. The tool then delivers HCC coding recommendations with 97% accuracy at the point of care, helping to increase coding accuracy and increase the average provider’s RAF scores by 35%.
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