CLINICAL APIs FOR HEALTHCARE RISK ADJUSTMENT CODING

Streamline clinical diagnosis coding and documentation with
easy-to-get-started API solutions for healthcare

API

Clinical APIs For Healthcare Risk Adjustment Coding: What you need to know?

Why clinical application for healthcare risk management?

APIs are designed to be easily integrated into existing systems, making it a perfect solution for those who want to improve their current risk management (RA) practices. With API, you don’t need to replace your entire system or change the way you work; instead, you can simply add new functionalities as needed.

Another key advantage of using API is that it allows for seamless communication between different applications and systems. By leveraging APIs in your clinical risk management strategy, you can create a unified system that shares data across multiple platforms and devices. This not only makes it easier to track patient information but also enables healthcare providers to make more informed decisions based on real-time data.

Moreover, APIs provide a secure way to share information between different parties while maintaining confidentiality and privacy regulations. It also reduces the cost associated with developing custom software by providing ICD-10-CM & HCC coders access to pre-built libraries of code.

How clinical APIs are being used in healthcare risk adjustment settings?

When it comes to healthcare risk adjustment, clinical APIs offer a wealth of possibilities for improving patient outcomes and reducing costs. Here are just a few ways that these powerful tools are being used in healthcare settings:

  1. Streamlining data collection: By using clinical APIs to automate the process of collecting patient health information, providers can get a more complete picture of their patients’ health status without burdening them with additional appointments or paperwork.
  2. Enhancing predictive analytics: Clinical APIs can be used to access large datasets of patient health information, which can then be fed into predictive models to help identify patients who may be at greater risk for certain conditions or complications.
  3. Improving care coordination: By sharing data through secure API connections between different providers and organizations, clinicians can work together more effectively to manage complex cases and provide better continuity of care.
Why RAAPID’s clinical API solutions for risk adjustment in healthcare?

HIPAA compliance is a critical aspect of healthcare technology, and RAAPID’s clinical API solutions for risk adjustment in healthcare are designed to meet the stringent requirements of this regulation. By leveraging secure data transmission and storage methods, RAAPID ensures that sensitive patient information remains protected at all times.

What sets RAAPID APIs apart from other risk adjustment APIs is its ability to seamlessly integrate with existing health IT systems, enabling healthcare providers to access accurate patient data quickly and efficiently. This allows for more precise diagnosis coding and improved risk scoring accuracy, leading to better decision-making for care of patients.

Moreover, HIPAA-compliant APIs allow healthcare organizations to securely share data across different platforms without fear of violating privacy regulations. Patient data information security has becomes increasingly important in today’s healthcare landscape, RAAPID’s healthcare API solutions offer a streamlined approach that ensures seamless collaboration among providers and payors while keeping patient privacy at the forefront.

To know more about the benefits and usage of clinical API in healthcare:

Our Risk Adjustment Coding APIs

ICD-10 CM/PCS

ICD-10 CM/PCS

Accurately extract billable
ICD-10-CM and PCS codes from patient encounter documents in real time

HCC & RAF APIs

HCC & RAF APIs

Accurately extract HCC and Risk (RAF) score codes from medical patients' encounter documents and medical claims in real-time

E&M APIs

E&M APIs

Clinical NLP API to identify Evaluation & Management codes and extract them from patient encounter documents

LOINC APIs

LOINC APIs

Clinical NLP API to accurately convert extracted clinical entities into LOINC codes

Named Entity Recognition

Named Entity Recognition

Extract clinical entities, its context and relationship from large unstructured & structured datasets

SNOMED CT APIs

SNOMED CT APIs

Clinical NLP API to accurately convert extracted clinical entities into industry standard SNOMED CT codes

CPT/HCPCS APIs

CPT/HCPCS APIs

Accurately extract procedural terminology codes (diagnostic/medical/ surgical) from the patient document

RxNorm APIs

RxNorm APIs

Accurately convert extracted clinical entities into industry standard RxNorm codes

PHI APIs

PHI APIs

Deidentify personal information as per safe Harbor guidelines and returns PHI redacted data

Custom APIs

Custom APIs

We also develop customized
clinical APIs as per your
custom requirement

Make the Most out of RAAPID’s AI Risk Adjustment Technology

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Years of Healthcare & AI Experience
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Healthcare Clients Served
> 0 X
Return On Investment
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Increased Efficiency And Productivity
> 0 %
Complete, Defensible Risk Scores
> 0 %
Reimbursement Accuracy

Who can use our clinical API healthcare solutions?

NLP for healthcare providers

For Healthcare Providers

Accurately extract billable codes from patient encounter documents and reduces the risk of errors that could lead to denied claims or reduced reimbursement.

NLP for medical coding companies

For Medical Coding Companies

Analyze unstructured data in medical records and claims forms automatically to improve HCC coding accuracy while reducing costs and turnaround times.

NLP

For Health Plans

Identify the key risk factors for each patient - such as age, gender, medical history, and current health status - to generate accurate HCC codes and RAF scores.

NLP for Risk Adjustment Vendors

For HealthTech Companies

Enhance ICD-10-CM & HCC coding capacity to ensure identifying all relevant diagnoses and conditions in a given patient population.

Use Cases

Provider.1
Provider

Trained over millions of real clinical notes, our clinical NLP platform has been leveraged to built various last mile applications such as:

  • Computer Assisted Coding
  • Computer Assisted CDI
  • Code Auditing
  • Quality Measures
  • Analytics
C 1
Payor

With our ability to understand & accurately parse unstructured & structured notes, our clinical NLP platform has powered numerous applications for Health plans such as:

  • AI Powered Risk Adjustment Coding
  • Pre Bill Chart Auditing
  • Population Health
  • Prior Authorization
  • HEDIS & Stars
D 1
Pharma & Lifescience

Expedite digital transformation journey by digitizing unstructured clinical notes using NLP. Improve ML, DL and Predictive models by leveraging hidden information trapped into the unstructured text to support use cases such as:

  • Clinical Trials Management
  • Drug Discovery
  • Clinical Research
  • Genome Sequencing
E 1
And Many More

If you are looking to understand the clinical language and parse unstructured texts, accurately and at scale, RAAPID’s clinical NLP APIs can help your growth. Let’s talk!

  • Semantic Search
  • Predictive Analytics
  • Clinical Decision Support
  • Medical cohort Analysis
API

Create a unified & secured system to share patient health information across multiple platforms and devices

FAQ’s related to clinical API for healthcare

From improved access to patient data to enhanced communication among stakeholders in the value-based care process- implementing these tools can help ensure efficient delivery of quality care while mitigating potential risks associated with inaccurate diagnosis coding, patient-care reimbursements or inadequate treatment planning.

There are numerous ways in which clinical application programming interface solutions are benefiting the healthcare risk adjustment space. First, they facilitate seamless communication between different electronic health record (EHR) systems, enabling providers to extract comprehensive patient data and relevant insights that aid in identifying patients who require more intensive care.

Secondly, APIs allow for more accurate diagnosis coding through the integration of advanced analytics algorithms and machine learning techniques. This ensures a higher level of precision in identifying chronic conditions and comorbidities while reducing documentation errors.

Furthermore, clinical APIs enable better monitoring of patient outcomes by providing real-time access to critical patient information such as medication adherence rates, disease progression patterns, and treatment effectiveness.

Ultimately, API integration is transforming how healthcare organizations handle risk management by enabling them to quickly identify high-risk patients and provide targeted interventions that improve health outcomes while reducing overall costs. With continued advancements in technology and a greater emphasis on interoperability across EHR platforms, we can expect even more innovative applications of clinical APIs within the healthcare industry.

One way that clinical APIs for HCC coding can ensure accuracy in risk adjustment factor (RAF) score calculation is through the power of machine learning algorithms. These algorithms can help detect patterns in patient data and identify potential gaps or inaccuracies in their medical history. By leveraging this information, healthcare providers can make more informed decisions about a patient’s care plan and ensure that they receive appropriate levels of treatment.

There are several ways in which clinical APIs act as risk adjustment analytics solution. One of the key ways is by providing access to securely storing vast amount of patient data, including medical histories and treatment plans. This data can be analyzed to identify patterns and trends that may help healthcare providers better understand their patient populations.

Another way clinical APIs act as risk adjustment analytics is through predictive modeling. By using advanced algorithms and machine learning techniques, these tools can identify patients who are at high risk for certain conditions or complications. This information can then be used to develop targeted interventions or care plans that can improve outcomes while reducing costs.

Finally, clinical APIs for HCC coding also play an important role in population health management. By providing real-time access to patient data, these tools enable healthcare providers to track disease outbreaks, monitor medication adherence, and identify gaps in care delivery. This information can then be used to implement proactive measures that promote wellness and prevent illness within communities.

Look for clinical APIs that offer robust documentation, clear pricing structures, and strong security measures to ensure the protection of sensitive patient information.

In addition to technical considerations, healthcare providers must also consider factors such as vendor reputation and customer support when selecting a clinical AP for risk adjustment. Vendors with proven track records of delivering quality risk adjustment solutions and providing exceptional HCC coding services are more likely to meet their needs than those who don’t have such accolades.

Ultimately, the selection process is unique to each organization based on their specific needs and goals. But by taking into account these key considerations during the assessment process, they will be better equipped to choose the right clinical APIs risk adjustment in healthcare settings.

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