img

The Importance of Data Extraction in Healthcare

Whether it’s negotiating reimbursements with insurers or providing optimal patient care, data extraction can take your physician group or health system to the next level. Data extraction solutions provide a wealth of benefits to providers, including improving diagnosis accuracies, treatment efficiency, and streamlining clinical decision-making.

But automating data extraction and risk score calculations present a challenge. These efforts rely on leveraging vast quantities of information from both electronic health records (EHRs) and traditional medical records – which many providers struggle to achieve.

This article explores the role of data extraction and reveals how leading providers can automate the process, multiplying a range of benefits.

What Is Data Extraction?

In healthcare, your patients may see a wide range of providers and specialists, hopping from insurer to insurer. Their data can include lab results, previous and ongoing diagnoses, clinic results, notes from specialists, and more. In these extremely common cases, it can be difficult to perform data collection and consolidation to provide your patients with care that encapsulates them holistically.

This is where data extraction comes in. Data extraction refers to the process of collecting or retrieving varying types of data from a range of sources. Oftentimes, this data is disorganized or unstructured, making it difficult to extrapolate using the raw data alone.

With data extraction, you can easily process, consolidate, and refine this data to store in a centralized location. These locations can be on-site, cloud-based, or a combination of both.

Uses for Data Extraction in Healthcare

Once your data is extracted, sorted, refined, and centralized, you can build a predictive algorithm to apply predictive analytics and various techniques. This can assist with risk-based arrangements, using the data to anticipate what levels of care and required codes each patient will need. With this information, health policies can develop methods to ensure the proper care for each patient.

That’s not all–data extraction and their relevant tools can also:

  • Enable the study of genetic sequences
  • Reduce the rate for dynamic data processing
  • Automates analytical records, including disease records, demographics, and more
  • Make information, such a pharmaceutical records and comprehensive data, accessible to approved users, such as physicians

Data extraction exists in every industry, and in healthcare, it can completely transform your group’s or system’s data and care practices.

Benefits of Data Extraction and Mining in Healthcare

The Royal Bank of Canada Capital Markets reported that the healthcare industry produces approximately 30% of all global data. Experts estimate that by 2025, that percentage will rise to 36%. This means the amount of unfiltered, unsorted data is not going to decrease anytime soon.

Investing in a tool that streamlines your data extraction and analyses affords you with several key advantages.

Improve Your Treatment Efficiency

When you have your patient’s complete medical history at your fingertips, you increase your ability to provide top-quality care for your patients. Data extraction allows you to access data, unfiltered and otherwise, and consolidate it into an easy-to-digest way to analyze available treatment plans.

With a wealth of data that’s properly processed and sorted, you can compare the efficacy of possible treatment routes and select the best course of action.

More Accurate Diagnoses

Every reputable clinician knows that for every reason a patient books an appointment, there’s usually several other underlying conditions that influence their condition. With data extraction tools, such as HCC Assistant, you can automatically analyze data in both your EHR and external resources.

HCC Assistant pulls patient records that were generated by different healthcare providers, clinics, and lab results. Inferscience’s other software, the HCC Assistant, can analyze text from uploaded PDF documents and images using optical character recognition (OCR) technology. This allows you to view the full scope of your patient’s medical history and pinpoint the more accurate diagnosis.

Streamline Clinical Decision Making

The number of hospitals adopting clinical decision support systems (CDSS) is increasing. This is because these systems use a knowledge base, applying rules or utilizing machine-learning to make extrapolations based on the data analyzed.

Data extraction works with these systems, allowing clinicians to easily compare a patient’s medical history and symptoms with current research or even similar cases.

Risks of Non-Compliance During Data Extraction

Manual clinical data extraction is a major regulatory risk, primarily due to the increased potential for errors, inconsistency, and non-compliance. A few common issues include:

  1. Inaccurate and Incomplete Data: Manually extracting clinical data increases the risk of human error, such as missing relevant information or recording it incorrectly. These inaccuracies can lead to incomplete records, which may not meet regulatory standards for quality, impacting areas such as patient safety, treatment reporting, and reimbursement claims.
  2. Delayed Reporting: Manual data extraction is often time-consuming, which can lead to delays in reporting to regulatory agencies. For example, slow cancer registry data entry can result in late submissions, potentially leading to penalties or impacting eligibility for certain funding opportunities.
  3. Privacy and Security Risks: Handling sensitive patient information through manual processes increases the risk of data breaches or unauthorized access. Regulatory requirements, like HIPAA, mandate strict protections for patient data, and manual handling can make it more challenging to consistently uphold these safeguards.
  4. Variability and Lack of Consistency: Differences in how teams or facilities manually extract and manage data can lead to inconsistencies. Such inconsistencies make it difficult to meet regulatory requirements for standardized reporting, especially when regulatory bodies require uniformity across datasets.
  5. Limited Audit Trails: Regulatory compliance often requires a clear, traceable audit trail for any data extraction and modification. Manual methods make this difficult to maintain, complicating audits or inspections, and potentially exposing organizations to penalties if data authenticity cannot be verified.
  6. Inconsistent Medical Coding: Manual processes may result in inconsistent application of standardized medical codes, such as ICD-10 for diagnoses. This inconsistency can lead to regulatory non-compliance, particularly in reimbursement models that depend on precise coding for accurate payment and reporting.

Automating data extraction can significantly mitigate these issues by enhancing data accuracy, ensuring timely reporting, reducing security risks, and creating a clear audit trail, all of which are critical for regulatory compliance.

How Inferscience’s Data Extraction Tools Improve Your Medical and Healthcare Practice

Inferscience offers an array of data extraction tools that can seamlessly integrate into your current workflow and applications. HCC Assistant, for example, can integrate into any web-based EHR and provide analysis insights to the provider in the patient’s chart. This allows providers working with Medicare Advantage patients to maximize their reimbursement.

Additionally, Inferscience utilizes cutting-edge OCR technology and advanced natural language processing (NLP) to analyze both structured and unstructured data. This allows providers to identify diagnoses that may typically get overlooked, all while simultaneously displaying the relevant Hierarchical Condition Category (HCC) code suggestions.

Ready to streamline your workflows and practice? Contact Inferscience today for a complimentary demo!

Click to rate this post!
[Total: 0 Average: 0]