Care gaps mean missing or late care like screenings, vaccines, managing chronic illnesses, or preventive services.
These gaps can hurt patient health and lower quality scores such as HEDIS and Medicare Star Ratings.
These scores affect pay-for-performance payments.
Pay-for-performance programs pay healthcare providers for better quality and efficiency, not just more services.
Unlike the old fee-for-service way, value-based care rewards providers for good patient results and satisfaction at controlled costs.
According to Veradigm, value-based care focuses on managing health for groups of people, preventing illness, and coordinating care to lower unnecessary hospital visits and emergency room use.
For admins and owners, doing well on these scores means finding care gaps quickly, sorting patients by risk correctly, and documenting well for risk adjustment.
These things affect payments and the organization’s financial health and quality scores.
Data intelligence in healthcare means using data analysis, connected systems, and AI tools to collect, study, and use health data from many places.
This data comes from electronic health records (EHRs), insurance claims, pharmacy records, social factors, and real-time patient info.
Healthcare analytics platforms help manage large amounts of data, spot care gaps, track patient progress, and create useful reports.
For example, Arcadia Analytics handles over 170 million clinical records by linking 2,600 EHR and claim sources.
This gives medical practices full views of patients, good risk adjustment, and helps improve HEDIS and Star Ratings.
Admins and IT managers use healthcare analytics to find specific care gaps in both single patients and groups.
These platforms alert care teams about missed tests, poor medication use, or unmanaged chronic illness.
This data approach helps focus resources where they are needed to close gaps and raise care quality.
Risk adjustment is a way to pay by estimating expected healthcare costs based on how sick a patient is and their background.
Accurate risk coding means assigning the right diagnosis and procedure codes that match patient conditions.
This is important so that providers get proper payment in value-based care deals.
Veradigm’s healthcare risk tools show how risk adjustment analytics help make better patient risk profiles and support closing care gaps.
Their Risk Adjustment Analytics give real-time revenue estimates and gap closure rates by combining claims and clinical data.
This makes sure providers capture the full patient complexity.
If risk coding isn’t correct, healthcare groups may get underpaid for sick patients or face penalties for wrong reports.
Cotiviti’s DxCG Intelligence software is well-known for predicting risk scores and costs.
It helps providers document well to improve money and quality scores.
Also, automated coding tools using Natural Language Processing (NLP) and Optical Character Recognition (OCR), like Veradigm’s eChart Coder, speed up coding and improve accuracy.
They pull clinical details from both organized and free-text records.
Automation cuts down errors common in manual coding and helps keep submissions correct while lowering audit risks.
Using data intelligence and full risk coding together improves pay-for-performance results by:
Cotiviti’s Quality and Stars tools help health plans and providers move beyond static reports to actively improve clinical and financial results.
By linking quality data with teamwork tools, plans can watch and close care gaps well, raising quality scores and revenue.
Artificial intelligence (AI) and workflow automation have changed healthcare operations.
They reduce administrative work and improve accuracy.
In payer-provider teamwork, AI-powered agents handle prior authorizations, eligibility checks, medical coding, and claims processing.
This saves time and lowers denials.
Oracle Health’s AI apps show this change.
Their Prior Authorization Agent knows the rules, fills forms automatically, and sends requests digitally.
This removes slow faxes and follow-ups.
AI automation can save providers hundreds of millions every year by cutting admin costs, which total about $200 billion in US healthcare.
AI also checks patient insurance in real-time, helping stop surprise bills.
Coding agents assign correct DRG and diagnosis codes and apply payer rules to avoid mistakes.
Claims agents make sure charges follow rules and reduce claim denials by using payer rules early.
For admins and IT managers, AI and automation bring clear benefits:
AI tools also back value-based care by linking payer data on risk coding and care gaps into clinical work.
This helps target outreach, improve risk adjustment, and boost pay-for-performance metrics like HEDIS or Medicare Star Ratings.
Health informatics helps collect, get, and share clinical data among doctors, nurses, admins, payers, and patients.
Through EHRs and data exchanges, organizations get current patient info quickly and safely.
Veradigm’s eChart Integration connects over 400,000 providers across EHR systems.
This automates medical record sharing, cutting manual data work and speeding chart retrieval.
Quick chart access is key to closing gaps and adjusting risk on time.
Such data sharing improves teamwork, lessens delays, and helps meet rules.
Safe data sharing also aids population health management by letting analytics pull claims, clinical records, pharmacy data, and social factors together.
Groups like Socially Determined score social risks to include non-medical factors in care plans.
This supports personalized care.
Even with clear advantages, adding data intelligence and risk coding to healthcare work is not easy.
Data comes from many places; different EHR systems and lack of connection cause problems.
Providers and payers must manage:
Healthcare leaders should pick vendors carefully, looking at how well they connect, grow with needs, support users, and follow rules.
Companies like Cotiviti, Veradigm, Arcadia Analytics, and Oracle Health offer full solutions with good records in risk management, quality, and revenue.
For healthcare admins, owners, and IT managers in the US, using data intelligence and risk coding together is key to closing care gaps and improving pay-for-performance results.
Using healthcare analytics, AI automation, and health informatics can improve patient care quality, lower admin work, and optimize payments under value-based care.
Handling ongoing challenges in data combining and workflow change is important to get the most out of these tools.
As healthcare changes, tech solutions will play a bigger role in delivering good patient care and steady financial results.
Oracle Health’s AI-powered applications aim to accelerate payer-provider collaboration, reduce claims denials, lower administrative costs, and enhance care coordination to improve value-based care and optimize resource allocation.
Administrative costs related to healthcare billing and insurance are estimated to be approximately $200 billion annually, driven by complex processing rules and inefficient manual workflows.
AI agents embed payer-specific business rules in provider workflows, enabling accurate prior authorizations, eligibility verification, medical coding, and claims submissions, resulting in higher clean claim rates and fewer denials.
The processes include prior authorization, eligibility verification, coverage determination, medical coding, claims processing, and denial management.
It discovers prior authorization needs, retrieves documentation requirements, auto-fills information for review, and digitally submits requests, eliminating faxes and follow-ups to streamline approvals.
The Eligibility Verification Agent provides accurate eligibility and coverage details at the point of care, helping avoid surprise billing and allowing providers to recommend covered treatments and programs.
It autonomously generates medical, diagnosis, and DRG codes and applies payer-specific coding guidelines to reduce errors and facilitate accurate billing.
The Charge, Contract, and Claims Agents collaborate to ensure accurate charge capture and compliant claims submission, embedding payer rules to generate clean claims and reduce processing time.
Oracle Health Data Intelligence integrates payer insights on risk coding and care gaps directly into provider workflows, helping close care gaps and improve pay-for-performance metrics like HEDIS.
It replaces manual medical record transmission with a centralized, secure network, allowing real-time access to encounter data and eligibility validation, improving administrative efficiency and data security.
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