Data Analytics for Healthcare

Data Analytics for Healthcare

An ever-increasing volume of digitized health information presents both challenges and opportunities across the healthcare industry. Global health organizations must make faster, more informed decisions to improve quality of care and contain costs.

Altair’s Data Analytics solutions help reduce health IT complexities and add efficiencies in areas like claims/reimbursement processing, interoperability, patient adherence and satisfaction analysis, physician performance analysis, and revenue cycle management.

Altair Data Preparation

Altair allows healthcare organizations to access, cleanse, and transform data—helping to break down data application silos and building automated workflows into standardized, shareable assets for optimizing strategic planning, streamlining operations, and maximizing resources.

  • Automatically reconcile disparate reporting formats such as Excel, PDF files, and other semi-structured files needed to achieve regulatory compliance
  • Reduce repetitive, manual effort with automated workflow models
  • Easily merge EMR, demographic, clinical, primary market research, and other disparate patient data sources
  • Track resources to anticipate need and deploy rapid, targeted responses
  • Evaluate new patient data to ensure accurate claims reporting
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Altair Knowledge Studio Machine Learning (ML) and Artificial Intelligence (AI)

Altair’s no-code ML and AI solution allows users to easily generate and share insights derived from financial, administrative, operational and clinical data to reduce risk and improve overall quality of care. Transparency in the model building process and explainable AI means everyone can understand the model, how predictions are made—and make confident decisions.

  • Drive value-based outcomes for patients and regulatory/reimbursement efficiencies
  • Pinpoint predictive behaviors to manage long-term costs and revenue growth, tailor effective communication, solve complex business problems and improve therapeutic adherence
  • Predict patient utilization patterns, identify patients at risk for readmittance and develop strategies to reduce unnecessary, expensive emergency/ urgent care visits
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Applying Predictive Analytics to Healthcare Data

Improve ROI and streamline wasteful administrative and manual data-driven processes in a telehealth environment.

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Revenue Cycle Management

Revenue cycle management becomes complicated and delayed by inaccessible data hosted in siloed and proprietary financial systems, in remittance files such as US-based 835 transaction files, claims paperwork, and third-party payment information from insurance companies and patients.

Altair data transformation automates repetitive, error-prone processes associated with accessing and reconciling financial healthcare data, eliminating manual work, and accelerating the revenue cycle.

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For Providers and Health Systems

Providers often rely on multiple, often proprietary, legacy applications to help process hospital finances, operational reporting, and outbound claims and inbound remittances. Our data prep solutions help large health systems and private practices alike do more with less by automating manual, error-prone workflows to rapidly process disparate data sources into a trusted, shareable asset.

With predictive analytics and ML, providers can anticipate and reactively scale resource and utilization needs (such as number of beds and staffing) as well as identify and target at-risk patient populations with proactive outreach to help reduce costly ER visits and readmissions.

For Insurers and Payers

With the transition to and focus on value-based care in the healthcare industry, payers must optimize strategies to manage cost and risk while driving value for patients.

Insurers can collate and transform volumes of clinical, demographic, and operational data, allowing for a full picture of patients. Compare and contrast schedules of benefits with Altair’s data prep solutions to adjust programs in reaction to market disruptors, such as regulatory and reimbursement changes, or in preparation for negotiations.

Predictive modelling on unmet need and patient trends can aid payers in assessing costs and high-risk members by geography and program type to anticipate pricing adjustments.

For Pharma and Biotech

Globally, life sciences are challenged with an ever-increasing focus on the importance of advanced analytics. Altair allows professionals at all skill levels to master data prep, AI, and machine learning in a no-code environment, so organizations can execute strategic decisions across commercial areas and improve the efficiency of financial back-office functions.

Brand and Market Access teams focused on optimizing the global launch and uptake of a new asset can collate and blend disparate data sources, such as claims, EMR, primary market research, etc., to track analogue usage across lines of therapy and anticipate success. Prepare for a successful launch by analyzing commercial and regulatory trends, ensuring compliance and predicting adherence compared to competitors.