Data extraction allows healthcare organizations to introduce better data utilization strategies to improve care standards, augment reimbursable claims, and reduce expenses. Using an advanced, accurate, and reliable data collection tool can address many of the long-standing issues where data is siloed, uncoordinated, and retained in different formats or structures, making it difficult to collate all the information about a patient in one central place.
Good practice standards indicate that the best diagnosis suggestions tool for HCC coding will use a triple approach, incorporating three systems that implement effective change and significant improvements in efficiencies, enabling data to be logged, analyzed, and validated for use across the scope of diagnosis, patient interventions, treatment protocols, and billing administration.
What Are the Three Systems Used in Advanced Data Extraction?
Rather than simply accessing data that relates to a patient’s medical condition, the ideal approach is to aggregate all the available information into one system that can be used for analytics and risk adjustment calculations. This method ensures data is not simply extracted but is organized into the most useful and accessible format, supporting faster clinical decision-making, more precise billing, and detailed patient records.
- Data analytics: Technology systems can collect data and then assess the compatibility and cross-uses of that data, normalizing values where different units of measurement have been used, for example, so the collected data is transferable, relevant, and coordinated.
- Supporting evidence: HCC coding reports submitted by healthcare organizations must include verifications or other corroborative records that demonstrate correct coding. Data extraction systems can create ordered information that shows its source, where the compiled data requires no further manipulation or work.
- Change tracking: Annual reports will inevitably change where patient conditions, treatments, and diagnoses evolve. Excellent data extraction ensures records are up to date, traces those changes, and delivers reports in a uniform manner, which can be valuable for physicians to identify the right change to treatment plans.
Importantly, data should be extracted from legacy systems alongside databases currently in use–mapping the whole view of the patient’s health. This factor is essential to capture data related to family status codes, indicating a likelihood of a condition developing that should be screened for and factored into patient care cost projections.
Data Extraction as an Element of Healthcare Digitization
Healthcare reporting is increasingly digital, with multiple advantages in terms of speed, security, and compliance with HIPAA patient confidentiality regulations. Now a mandated aspect of healthcare services across much of the sector in the US, providers use electronic healthcare records (EHRs) to increase the visibility of data, ensuring information can be shared with colleagues, transferred to partner organizations, and used to meet reporting requirements.
Due to the vast number of variables present in medical data, including more than 70,000 HCC coding options, the administrative burden can command almost eight times the resources necessary in other industries. Automated and intelligent data extraction can streamline this resource drain and ensure healthcare organizations maintain control over operational spending while expediting processes that lead to improvements in patient care standards.
Those benefits are realized by making processes faster and less exposed to error, improving care quality and patient outcomes, and reducing cost outgoings while recouping reimbursements that may have otherwise been lost.
Solving the Complications of Healthcare Data Extraction
Manually transferring data from one system to the next or relying on outdated reporting can cause no end of inefficiencies, from utilizing the limited time of skilled clinical staff to data processing errors and incorrect data labeling or categorization.
Software solutions designed for data extraction cover the scope of medical, pharmaceutical, and past records, ensuring that current databases are comprehensive and reliable and offer fast data access for approved users. Other use cases include predictive analytics to anticipate the potential for a disease or condition to develop, more targeted healthcare delivery and budgeting, and automated billing and data synchronization across systems.