Data strategies and data extraction practices can transform and streamline your daily operations in the healthcare and medical industries. According to a 2019 survey conducted by KPMG Global Proptech, only 25% of survey respondents have a ‘well-established’ data strategy, and only 52% of respondents reported having a structured approach to using data to improve their decision-making capabilities.
In the healthcare and medical industries, these decisions can have a major impact on the lives of patients and providers. Data can be used to paint a holistic picture of a patient, target possible diagnoses that may have been otherwise missed, and even aid in insurance purposes–such as the risk adjustment factor score.
Utilizing your data so that it can improve your daily operations and provide productive insights into your decision-making capabilities is critical. This process can be started by implementing a data collection process and can be broken down simply into four steps: collect, curate, analyze, and act.
Data collection is the process of gathering and evaluating information or data in an established, systematic way that makes it easier to answer stated research questions, test hypotheses, and evaluate outcomes. In terms of the healthcare industry, this looks like collecting, analyzing, and using the data for patient documentation and resources.
If your organization or agency does not currently have clearly-defined data collection processes, you should begin with the four stages of data collection.
Core data (a model layer technology) can be stored in many places, including personal drives, data warehouses, the cloud, and even email attachments. Because data can be stored in a variety of storage silos, this can make navigating your data difficult.
Your data collection process must begin with a standardized way of collecting and storing your data sets. A common form of streamlined collection is a data tool that allows users to ‘drag and drop’ external files, new files, and more into a centralized database. This makes it easier to import data and trigger the beginning of a workflow process.
Data curation is the active management of data through its life-cycle, interest, and usefulness to your field. With effective data curation, data discovery, retrieval, and reuse is easier to maintain. Because this is difficult to do manually, most organizations rely on management tools managed by machine learning, which automatically stores and standardizes common data models.
This stage is frequently visited by clinicians because it focuses on identifying trends, tracking changes, and making predictions. This is where the data transforms from numbers or findings in a report and into usable, applicable insights. In this stage, you compare and contrast the data, and find what’s applicable to your specific patient or scenario.
With all of your information and data collected, curated, and analyzed, it’s time to make a decision. This stage refers to when clinicians or providers make decisions based on the information they’ve acquired and analyzed from the provided datasets. However, in the healthcare field, each decision and action can make a lasting impact on a patient. That’s where Inferscience’s high-quality tools and resources come in.
With tools like Infera offered by Inferscience, clinicians now have a clinical decision support system to help them understand their data in congruence with a patient’s electronic health record (EHR). Providers can confidently treat patients with this HIPAA-compliant tool that provides them with peer-reviewed, evidence-based clinical recommendations so that no stone is left unturned.
Looking for more support system insights, including data extraction solutions and claims assistant? Contact Inferscience today at info@inferscience.com for your free demo.
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